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quantumclaw/clawhdf5:main quantumclaw/clawhdf5:feat/p1-proof quantumclaw/clawhdf5:fix/p1-read-gaps quantumclaw/clawhdf5:feat/p1-conformance-report quantumclaw/clawhdf5:fix/phase0-correctness quantumclaw/clawhdf5:docs/withdraw-zeroclaw quantumclaw/clawhdf5:chore/remove-agent-feature
quantumclaw/clawhdf5:v2.7.0 quantumclaw/clawhdf5:v2.6.0 quantumclaw/clawhdf5:v2.5.0 quantumclaw/clawhdf5:v2.4.0 quantumclaw/clawhdf5:v2.3.0 quantumclaw/clawhdf5:archive/clawmates-mission-01a00cfa-c69f39fd-i2 quantumclaw/clawhdf5:archive/clawmates-mission-01a00c57-4c1f211e quantumclaw/clawhdf5:archive/clawmates-mission-01a00bbb-haiku-baseline quantumclaw/clawhdf5:archive/clawmates-mission-019ff5b1-1d1c5b0f quantumclaw/clawhdf5:v2.2.0 quantumclaw/clawhdf5:pre-rewrite-2026-06-05 quantumclaw/clawhdf5:v2.1.0
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compare: quantumclaw/clawhdf5:archive/clawmates-mission-01a00cfa-c69f39fd-i2
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quantumclaw/clawhdf5:feat/p1-proof quantumclaw/clawhdf5:main quantumclaw/clawhdf5:fix/p1-read-gaps quantumclaw/clawhdf5:feat/p1-conformance-report quantumclaw/clawhdf5:fix/phase0-correctness quantumclaw/clawhdf5:docs/withdraw-zeroclaw quantumclaw/clawhdf5:chore/remove-agent-feature
quantumclaw/clawhdf5:v2.7.0 quantumclaw/clawhdf5:v2.6.0 quantumclaw/clawhdf5:v2.5.0 quantumclaw/clawhdf5:v2.4.0 quantumclaw/clawhdf5:v2.3.0 quantumclaw/clawhdf5:archive/clawmates-mission-01a00cfa-c69f39fd-i2 quantumclaw/clawhdf5:archive/clawmates-mission-01a00c57-4c1f211e quantumclaw/clawhdf5:archive/clawmates-mission-01a00bbb-haiku-baseline quantumclaw/clawhdf5:archive/clawmates-mission-019ff5b1-1d1c5b0f quantumclaw/clawhdf5:v2.2.0 quantumclaw/clawhdf5:pre-rewrite-2026-06-05 quantumclaw/clawhdf5:v2.1.0
1 Commits
fix/p1-read-gaps .. archive/clawmates-mission-01a00cfa-c69f39fd-i2
Author SHA1 Message Date
clawhdf5 research phase 563cdd2178 research: implementation brief for perf/security/provenance pass (INT-01..19) 
Read against ROADMAP.md/IMPROVEMENT_LOG.md/CLAUDE.md/CHANGELOG.md first so
nothing already-merged is re-proposed. 19 numbered INT items grouped by
crate (clawhdf5-agent provenance wiring, clawhdf5-format/io untrusted-file
parsing, clawhdf5-ann HNSW hot path, clawhdf5-py mutex poisoning), each with
file path, problem, and proposed change, plus a suggested implementation
order for the coding phase.
 2026-08-16 23:57:29 +00:00
236 changed files with 6739 additions and 38321 deletions
Expand all files Collapse all files
.gitea/workflows/ci.yml
+9 -69
View File
@@ -9,78 +9,18 @@ jobs:
runs-on: ubuntu-latest
container: rust:latest
steps:
# Plain git rather than actions/checkout: that is a JavaScript action,
# and rust:latest has no `node`, so it failed with exit 127 before any
# code was built — on every push. actions/cache went for the same reason.
- name: Check out
run: |
git init -q .
git remote add origin "${GITHUB_SERVER_URL}/${GITHUB_REPOSITORY}.git"
for i in 1 2 3; do git fetch -q --depth 1 origin "${GITHUB_SHA}" && break; sleep 5; done
git checkout -q FETCH_HEAD
- uses: actions/checkout@v4
- name: Cache cargo registry/target
uses: actions/cache@v4
with:
path: |
~/.cargo/registry
~/.cargo/git
target
key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
- name: Install rustfmt & clippy components
run: rustup component add rustfmt clippy
- name: Install thumbv7em-none-eabihf target
run: rustup target add thumbv7em-none-eabihf
- name: Install Python interop dependencies
# The interop suites used to skip silently when python3/h5py were
# missing, so they never ran in CI. Install them and make a missing
# dependency a failure (CLAWHDF5_REQUIRE_INTEROP below).
run: |
apt-get update
# cmake builds libz-ng-sys for the opt-in `fast-deflate` (zlib-ng)
# steps in ci-test.sh; rust:latest does not ship it. The default
# build (pure-Rust zlib-rs) does not need it.
apt-get install -y --no-install-recommends python3 python3-venv cmake
python3 -m venv /opt/interop
/opt/interop/bin/pip install --no-cache-dir h5py numpy netCDF4 xarray hdf5plugin
echo "/opt/interop/bin" >> "$GITHUB_PATH"
- name: Show interop library versions
run: /opt/interop/bin/python -c "import h5py, netCDF4, hdf5plugin; print('h5py', h5py.__version__, 'HDF5', h5py.version.hdf5_version, 'netCDF4', netCDF4.__version__, 'hdf5plugin', hdf5plugin.version)"
- name: Run CI script
env:
# Name the interpreter outright rather than relying on $GITHUB_PATH
# reaching the test processes: if `python3` resolved to the system
# one instead of the venv, every interop suite would skip.
# CLAWHDF5_REQUIRE_INTEROP turns that skip into a failure, so the
# two together mean the suites either run or the build goes red.
CLAWHDF5_PYTHON: /opt/interop/bin/python
CLAWHDF5_REQUIRE_INTEROP: "1"
run: bash scripts/ci-test.sh
test-arm64:
# The aarch64 kernels in clawhdf5-accel — NEON `dot_i8`, including the
# SDOT path, and the f32 NEON kernels — are cfg'd out on x86, so the job
# above never compiles, lints or tests them.
#
# `linux_arm64` is served by two runners that execute differently:
# vision-01 runs steps on the host (Rust already installed) and vision-02
# runs them in docker.gitea.com/runner-images. So the steps work in both:
# no `container:`, no JavaScript actions (they are fetched from GitHub,
# which not every runner reliably reaches), and an explicit `+stable`
# toolchain rather than whatever a host happens to default to.
runs-on: linux_arm64
env:
CARGO_NET_RETRY: "10"
CARGO_TERM_COLOR: always
steps:
- name: Check out
run: |
git init -q .
git remote add origin "${GITHUB_SERVER_URL}/${GITHUB_REPOSITORY}.git"
for i in 1 2 3; do git fetch -q --depth 1 origin "${GITHUB_SHA}" && break; sleep 5; done
git checkout -q FETCH_HEAD
- name: Rust stable
run: |
export PATH="$HOME/.cargo/bin:$PATH"
command -v rustup >/dev/null || curl -sSf --retry 5 https://sh.rustup.rs | sh -s -- -y --profile minimal --default-toolchain none
rustup toolchain install stable --profile minimal --component clippy
echo "$HOME/.cargo/bin" >> "$GITHUB_PATH"
- name: Confirm aarch64
run: |
test "$(uname -m)" = aarch64
if grep -q asimddp /proc/cpuinfo; then echo "dot-product extension present: SDOT kernel runs"; else echo "no dot-product extension: plain NEON kernel runs"; fi
- name: Clippy (aarch64 kernels)
run: cargo +stable clippy -p clawhdf5-accel --all-targets -- -D warnings
- name: Test
run: cargo +stable test -p clawhdf5-accel -p clawhdf5-ann -p clawhdf5-format
.gitea/workflows/conformance.yml
-56
View File
@@ -1,56 +0,0 @@
name: Conformance
# Nightly: read every file of the pinned public HDF5 corpora with clawhdf5 and
# with h5py/libhdf5 and compare (conformance/run.sh; CONFORMANCE.md explains
# the method). Fails on any panic, hang, crash or out-of-memory in clawhdf5,
# and when the ok count drops below conformance/baseline.json or a file the
# baseline lists as ok stops being ok. The report is printed into the job log;
# nothing is uploaded (artifact actions are JavaScript, which rust:latest
# cannot run — see CLAUDE.md).
on:
schedule:
- cron: "17 3 * * *"
workflow_dispatch:
jobs:
conformance:
runs-on: ubuntu-latest
container: rust:latest
timeout-minutes: 60
env:
CARGO_NET_RETRY: "10"
steps:
# Plain git, not actions/checkout (a JavaScript action; see ci.yml).
- name: Check out
run: |
git init -q .
git remote add origin "${GITHUB_SERVER_URL}/${GITHUB_REPOSITORY}.git"
for i in 1 2 3; do git fetch -q --depth 1 origin "${GITHUB_SHA}" && break; sleep 5; done
git checkout -q FETCH_HEAD
- name: Install h5py, h5dump and the probe's codec libraries
# hdf5-tools: h5dump for the CVE-corpus comparison. libaec-dev and
# pkg-config: the probe builds clawhdf5-format with `szip` (the core
# crates' default build needs neither).
run: |
apt-get update
apt-get install -y --no-install-recommends python3 python3-venv hdf5-tools libaec-dev pkg-config
python3 -m venv /opt/conformance
/opt/conformance/bin/pip install --no-cache-dir -r conformance/requirements.txt
/opt/conformance/bin/python -c "import h5py, hdf5plugin; print('h5py', h5py.__version__, 'HDF5', h5py.version.hdf5_version, 'hdf5plugin', hdf5plugin.version)"
h5dump --version
- name: Probe unit tests
run: cargo test --release --manifest-path conformance/probe/Cargo.toml
env:
CARGO_TARGET_DIR: conformance/.cache/target
- name: Sweep
# The corpora come from GitHub (pinned commits, conformance/corpus.txt),
# so this job needs a runner that reaches github.com.
env:
CLAWHDF5_PYTHON: /opt/conformance/bin/python
run: bash conformance/run.sh
- name: Report
if: always()
run: |
if [ -f CONFORMANCE.md ]; then cat CONFORMANCE.md; else echo "no report was generated"; fi
if [ -f conformance/.cache/results/summary.md ]; then
echo; echo "---- per-file detail (conformance/.cache/results/summary.md) ----"
cat conformance/.cache/results/summary.md
fi
.gitignore
-1
View File
@@ -4,4 +4,3 @@ benchmarks/longmemeval/*.json
# Local model weights (MiniLM etc.) — large, not committed
weights/
.venv
BENCHMARKS.md
+140 -1398
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File diff suppressed because it is too large Load Diff
CHANGELOG.md
-1112
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File diff suppressed because it is too large Load Diff
CLAUDE.md
+8 -148
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@@ -1,7 +1,7 @@
# clawhdf5
## Purpose
Pure-Rust HDF5 format implementation with HNSW vector search, WAL-backed persistence, agent memory storage, and GPU-accelerated vector search. A standalone library. Its one verified consumer is ClawBrainHub (`.brain` files); no agent framework integrates it (OpenClaw and ZeroClaw claims were withdrawn on 2026-09-25 — neither was ever true).
Pure-Rust HDF5 format implementation with HNSW vector search, WAL-backed persistence, agent memory storage, and GPU-accelerated I/O. Used by ZeroClaw as its persistent memory and knowledge graph backend.
## Architecture
@@ -11,15 +11,15 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
|-------|------|
| `clawhdf5-format` | HDF5 binary spec parser (superblock, B-tree, heap) — also holds shared type definitions and physical constants |
| `clawhdf5-io` | Read/write implementation |
| `clawhdf5-filters` | Deflate backends (zlib-rs, zlib-ng, Apple Compression); the HDF5 filter pipeline and the other codecs (LZ4, Zstd, SZIP, N-Bit, scale-offset, pcodec) live in `clawhdf5-format`. No Blosc. |
| `clawhdf5-filters` | Compression filters (gzip, LZ4, Zstd, Blosc) |
| `clawhdf5-derive` | Proc-macro derive for HDF5-serializable structs |
| `clawhdf5` | Main facade crate |
| `clawhdf5-netcdf4` | NetCDF-4 compatibility layer |
| `clawhdf5-ann` | HNSW approximate nearest-neighbor vector index |
| `clawhdf5-agent` | Agent memory, session history, knowledge graph storage |
| `clawhdf5-gpu` | GPU vector distance computation via wgpu (hand-written WGSL compute shaders) — not dataset I/O |
| `clawhdf5-gpu` | GPU-accelerated I/O via wgpu (hand-written WGSL compute shaders) |
| `clawhdf5-accel` | CPU SIMD acceleration path |
| `clawhdf5-migrate` | SQLite → HDF5 agent-memory migration |
| `clawhdf5-migrate` | Schema migration engine |
| `clawhdf5-android` | Android JNI bindings |
| `clawhdf5-cli` | Command-line interface |
| `clawhdf5-napi` | Node.js native addon bindings |
@@ -27,128 +27,14 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
| `clawhdf5-bench` | Benchmark suite |
## Key Features
- Zero-C-dependency HDF5 read/write: no libhdf5, and deflate defaults to
pure-Rust zlib-rs (`fast-deflate` opts into zlib-ng, which needs cmake).
`ci-test.sh` fails if a C-building crate enters the core crates' default
tree. flate2 must keep `runtime_detection` with zlib-rs — without it zlib-rs
loses SIMD and inflates 3.5x slower. MSRV is 1.92 (`rust-version`, checked
in CI).
- Zero-dependency HDF5 read/write (no libhdf5 C library required)
- HNSW vector index for semantic similarity search over agent memories — the
`clawhdf5-agent` `hnsw` feature is **on by default**, so `hybrid_search` uses
the approximate `clawhdf5-ann` index for the vector stage (the index mirrors
the cache and self-heals on drift). Build the agent with
`--no-default-features --features float16` to force the exact linear cosine scan.
The agent's `parallel` feature (also default) builds the index on a thread
pool; the graph is identical with or without it.
The index uses the HNSW paper's diversity heuristic for neighbour selection
(plain closest-M capped recall on clustered data: 0.31 recall@10 at 100K). Its
graph is saved to `<store>.h5.ann` at each checkpoint and reloaded by `open()`
(tied to the checkpoint by a generation id; stale/damaged sidecars are
ignored and the index rebuilt). `MemoryConfig::quantized_index` (**on by
default** for new stores, persisted; stores predating the setting load as
`false` and keep their f32 index — guarded by
`tests/fixtures/store_v2_5_0.h5`; CLI opt-out is `create --f32-index`)
stores the index's own copy of the embeddings as `i8`,
which roughly halves a loaded store's memory (2.72x -> 1.74x the raw vectors
at 100K); because quantised distances are approximate and `ef` cannot
compensate, the query path then re-scores the candidate pool against the
exact embeddings, which holds recall at the f32 index's level. It is also
faster at equal recall: 1.63x the QPS on x86-64 (AVX2) and 1.18x on a
Raspberry Pi 5 (`clawhdf5_accel::dot_i8`, NEON `SDOT` via inline asm since
the intrinsic is unstable; plain NEON on pre-dotprod cores). The aarch64
code is `cfg`'d out on x86, so x86 CI never compiles or lints it — test it
on real ARM (`rpivision02`, 10.0.2.3, is a Pi 5). `hybrid_search` keeps one incremental BM25
index for the life of the store and never writes the store: Hebbian
activation boosts are persisted by the next checkpoint (or on drop), not per
query. Measure any search-path change with
`cargo run --release -p clawhdf5-bench --bin search_harness` (baselines in
`BENCHMARKS.md`).
- WAL (write-ahead log) for crash-safe persistence, with a chained CRC32
trailer per entry (each entry's CRC folds in the previous entry's CRC) so a
corrupted, reordered, duplicated, or spliced entry stops replay cleanly
instead of loading bad or tampered data. The pre-chaining per-entry-CRC
format (v2) is still fully readable; the oldest no-CRC format (v1) is only
reachable through the one-time migration path in `HDF5Memory::open`, not
through the public `WalFile::read_entries`.
**What the WAL guarantees:** integrity, ordering, and recovery from a
*process* crash at any point — including between a checkpoint and the WAL
truncate (each checkpoint records a `WalMark` in `/meta`, and `open()` skips
the WAL prefix the `.h5` already contains, so entries are never applied
twice). Checkpoints and snapshots are made durable as a unit (temp file
synced, renamed, directory synced). **What it does not guarantee:**
individual WAL appends are *not* fsynced (a deliberate latency trade-off), so
saves made since the last checkpoint can be lost on power failure or kernel
panic. Current header version is 4 (adds the `Update` record used by
`save_or_update`); v3 files are read and upgraded in place.
- A store has a **single writer**: `HDF5Memory::create`/`open` hold an exclusive
advisory lock on `<store>.h5.lock` and a second opener gets
`MemoryError::Locked`. Use `HDF5Memory::open_read_only` for a lock-free,
never-writing point-in-time view (the CLI's `recall`/`stats`/`agents-md`/
`export` do). An unreadable WAL (torn header, bad magic) is quarantined to
`<store>.h5.wal.corrupt-<ts>` rather than blocking `open()`; a WAL with an
unknown *newer* version still fails and is left untouched.
- `MemoryConfig::float16` (**on by default** for new stores, persisted;
existing stores keep their recorded `false` — guarded by the v2.5.0
fixture in `tests/float16_store.rs`; CLI opt-out is `create --f32`) writes
`/memory/embeddings` as IEEE half precision (48% smaller file at 100K;
LongMemEval with real MiniLM embeddings identical to f32).
`MemoryCache::half_precision` rounds each embedding as it enters the cache (push, update, WAL replay, and on load of a store still
`f32` on disk), so memory and file agree bit for bit; the conversions live
in `clawhdf5_format::float16` and must stay the single implementation.
Values beyond ±65504 are `MemoryError::InvalidEntry`. Interop: every file
must open in h5py — `f32` datasets and empty datasets did not until
2026-09-23 (see `docs/known-issues.md`); the agent's `h5py_interop` test
guards a whole store.
- `HDF5Memory::search(query_emb, text, &SearchOptions)` is the full search
path: optional source-channel filter (applied before ranking; exact scan of
the allowed records whenever cheaper than `pool × M` index distance
evaluations, and as the fallback when the pool comes back short), fusion,
activation scaling, optional re-ranking and confidence rejection.
`hybrid_search`/`hybrid_search_with` are thin wrappers; `ClawhdfBackend`
(the `openclaw` module) is `search` with re-rank + confidence on.
- **OpenClaw is not supported** (decided 2026-09-25): clawhdf5 is not an
OpenClaw memory plugin and never was — the old `memory.backend = "clawhdf5"`
config was never valid. Don't reintroduce OpenClaw claims; `docs/openclaw.md`
records what a real plugin would need.
- **ZeroClaw does not use clawhdf5** (checked 2026-09-25 against upstream
v0.8.5 and the `osobh/zeroclaw` fork, and their full history): no
`clawhdf5` feature or backend exists; ZeroClaw's memory backends are
sqlite/lucid/postgres/qdrant/markdown/none behind its own `Memory` trait.
`clawhdf5-migrate`'s default SQLite layout (`memory_chunks`, `sessions`,
`entities`, `relations`) is not ZeroClaw's schema either (ZeroClaw's is a
`memories` table). Don't reintroduce integration claims without an
integration and a test against the real consumer. Measure changes with
`search_harness --options-study`.
- `MemoryConfig::compression` is off by default; when on, embeddings are
deflate-compressed, or Zstd with the agent's `zstd` feature (links libzstd).
- Signed checkpoints (`clawhdf5-agent` `signing` module): with
`HDF5Memory::set_signing_key` every checkpoint stores an Ed25519-signed
manifest (SHA-256 per record in a Merkle tree + settings/sessions/graph
hashes; per-record hashes in `/integrity/record_hashes`);
`HDF5Memory::verify(path, &pk)` locates edits. The hashes must cover exactly
what the file persists in the form the loader returns it (strings lose
trailing NULs; an empty WAL mark is not written) or untouched stores stop
verifying — `tests/signed_store.rs` round-trips awkward strings. The key is
never persisted; a signed store refuses to checkpoint without it
(`MemoryError::SigningKeyRequired`, and `MemoryError` is `#[non_exhaustive]`).
WAL entries after the checkpoint are not covered.
- `Dataset::verify_provenance()` (clawhdf5 facade, `provenance` feature, on by
default) recomputes a dataset's SHA-256 and compares it against the
`_provenance_sha256` attribute written automatically on save when
`DatasetBuilder::with_provenance` is used. It's opt-in per call, not run
automatically on open — it decodes and hashes the whole dataset. The hash
is unkeyed (tamper-*evident*, not tamper-*proof*): it detects accidental
corruption, not a deliberate actor able to modify both the data and the
stored hash.
- `clawhdf5-agent`'s `HDF5Memory::save`/`save_batch`/`save_or_update` run every
write through an in-memory (session-scoped, not persisted to disk)
provenance ledger and write-anomaly detector: a content hash per record
(`provenance.rs`) for detecting accidental mid-session corruption, plus
rate-limit/injection-pattern/source-distribution checks (`anomaly.rs`).
Alerts never block a save — drain them with `HDF5Memory::take_anomaly_alerts`.
`MemorySource` for this bookkeeping is inferred from the caller-supplied
`source_channel` string (a heuristic, not an authenticated trust boundary).
- GPU-accelerated vector distance computation (`clawhdf5-gpu`, wgpu); HDF5 I/O itself is CPU-only
- WAL (write-ahead log) for crash-safe persistence, with a CRC32 trailer per entry so a corrupted entry stops replay cleanly instead of loading bad data
- GPU-accelerated batch I/O for large dataset processing
- Python and Node.js bindings for cross-language use
- NetCDF-4 compatibility for scientific data interop
@@ -164,24 +50,6 @@ cargo build --release
cargo test --workspace
```
### CI
`.gitea/workflows/ci.yml` has two jobs, both green as of 2026-09-22:
- **`test`** (`ubuntu-latest`, in `rust:latest`) runs `scripts/ci-test.sh` with
the h5py/netCDF4 interop suites required (`CLAWHDF5_REQUIRE_INTEROP=1`).
Served by the `tank` and `architect` runners.
- **`test-arm64`** (`linux_arm64`) lints and tests the aarch64 code — the NEON
kernels are `cfg`'d out on x86, so this is the only place they are built.
Served by `vision-01` (host mode) and `vision-02` (Docker), so steps must
work in both.
Keep workflows free of JavaScript actions (`actions/checkout`, `actions/cache`,
…): `rust:latest` has no `node`, and not every runner reaches GitHub, where
they are fetched from. Check out with plain `git` instead. The `test` job
installs `cmake` for the opt-in `fast-deflate` (zlib-ng) steps; the default
build needs no C toolchain, so `test-arm64` does not.
All runners are on `gitea-runner` 3.5.0, from `docker.gitea.com/act_runner`
— `gitea/act_runner:latest` on Docker Hub is frozen at 0.6.1.
### CLI
```bash
cargo run -p clawhdf5-cli -- --help
@@ -196,12 +64,4 @@ python -c "import clawhdf5; print(clawhdf5.__version__)"
```
## Integration
- **ClawBrainHub** (`clawverse/clawbrainhub` on git.redclaw.dev) is the one
verified consumer: `cbh-core` reads and writes `.brain` files through the
facade (`File`, `FileBuilder`, `AttrValue`, `Selection`), `cbh-scanner`
uses the facade, and `cbh-cli` uses `clawhdf5_agent::bm25::BM25Index`. It
depends on this repo by path (`../clawhdf5`), so it builds against whatever
is checked out — changes to those APIs reach it directly. Verified
2026-09-25 against main: builds, and its 204 tests pass.
- OpenClaw and ZeroClaw were both described as consumers; neither integrates
clawhdf5 (see Key Features and `docs/openclaw.md`).
ZeroClaw imports this as a Cargo feature (`clawhdf5` feature flag) to persist agent memory with HNSW vector search for context retrieval.
CONFORMANCE.md
-300
View File
@@ -1,300 +0,0 @@
# clawhdf5 conformance report
Every HDF5 file of eight public corpora (pinned by commit) is read twice — by
clawhdf5 (`conformance/probe`, the same `clawhdf5-format` calls the facade
makes) and by h5py/libhdf5 (`conformance/ref.py`) — and the two readings are
compared object by object: the set of hard-linked objects, each dataset's and
attribute's shape, and a SHA-256 of its values in a canonical encoding. The
CVE corpus is also run through `h5dump`. Each side runs under a timeout and an
address-space limit, so a hang, crash or runaway allocation is recorded, not
fatal. This file is generated by `conformance/run.sh`; do not edit it by hand.
## Run
| | |
|---|---|
| date | 2026-09-26 03:46 UTC |
| clawhdf5 commit | `10d1029ead524e2fe64c2cd7f61b28067d9e449c` |
| machine | `tank`: AMD Ryzen 7 7800X3D 8-Core Processor, 16 CPUs, 61 GiB, Linux 7.0.0-34-generic x86_64 |
| command | `conformance/run.sh --no-fetch --update-baseline` |
| rustc | rustc 1.98.1 (48a229cea 2026-09-01) |
| reference | h5py 3.16.0, HDF5 2.0.0, numpy 2.5.3, hdf5plugin 7.1.0, Python 3.14.4 |
| h5dump | Version 1.14.6 (CVE corpus only) |
| limits | 20 s timeout (SIGKILL), 4096 MiB address space, per process; 16 files in parallel |
| runtime | 22 s probing + comparing (0 s fetch/build before it) |
## Results
A file's class is the first that applies:
- **panic / hang / crash / oom** — clawhdf5 panicked (caught per object or not), hit the timeout, died on a signal, or failed an allocation. The CI gate fails on any of these.
- **h5py-cannot-read** — libhdf5 could not open the file (or itself crashed or hung). Nothing to compare against; most are the deliberately malformed CVE reproducers.
- **our-error** — clawhdf5 returned an error for something h5py reads.
- **mismatch** — both read it, but the shapes, values, object set or attribute set differ.
- **ok** — every object h5py reads, clawhdf5 reads identically.
| corpus | files | ok | our-error | mismatch | h5py-cannot-read | panic | hang | crash | oom |
|---|---|---|---|---|---|---|---|---|---|
| NCAS-CMS_pyfive | 33 | 32 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| cve_hdf5 | 147 | 100 | 6 | 9 | 32 | 0 | 0 | 0 | 0 |
| h5py_data | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| hdf5 | 466 | 386 | 8 | 12 | 60 | 0 | 0 | 0 | 0 |
| netcdf-c | 20 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| netcdf4-python | 18 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| usnistgov_h5wasm | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| xarray-data | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| **all** | **697** | **569** | **14** | **22** | **92** | **0** | **0** | **0** | **0** |
2 of the 22 mismatches are a known h5py bug, not ours (see *Known not-our-bug*).
Corpora (fetched by `conformance/fetch-corpus.sh` into the gitignored `conformance/.cache/`):
| corpus | source | commit |
|---|---|---|
| hdf5 | https://github.com/HDFGroup/hdf5 | `a3cf1ea82cc7` |
| cve_hdf5 | https://github.com/HDFGroup/cve_hdf5 | `3fd1f5ae3869` |
| netcdf-c | https://github.com/Unidata/netcdf-c | `beb7b9585273` |
| NCAS-CMS_pyfive | https://github.com/NCAS-CMS/pyfive | `8cf07b874913` |
| usnistgov_h5wasm | https://github.com/usnistgov/h5wasm | `02f6336527d2` |
| netcdf4-python | https://github.com/Unidata/netcdf4-python | `6e67576d39ae` |
| xarray-data | https://github.com/pydata/xarray-data | `a35297e9da2c` |
| h5py_data | https://github.com/h5py/h5py (`h5py/tests/data_files`) | `b2f0347c4200` |
## Panics, hangs, crashes, out-of-memory
None.
## Our-error root causes
Grouped by normalised error message. *files* counts files whose class this cause affects.
| files | objects | error | examples |
|---:|---:|---|---|
| 6 | 6 | `UnsupportedFilter(N)` | `hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_blosc.h5`, `hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_blosc2.h5`, `hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_bshuf.h5` (+3 more) |
| 3 | 3 | `DataSizeMismatch { expected: N, actual: N }` | `cve_hdf5/cvefiles/cve-2020-18494.h5`, `cve_hdf5/cvefiles/cve-2024-32623.h5`, `cve_hdf5/cvefiles/cve-2025-2309.h5` |
| 2 | 2 | `ChunkedReadError("…")` | `cve_hdf5/cvefiles/cve-2025-2308.h5`, `hdf5/test/testfiles/bad_nbit_parms_walk.h5` |
| 1 | 1 | `UnexpectedEof { expected: N, available: N }` | `cve_hdf5/cvefiles/cve-2019-9151.h5` |
| 1 | 1 | `MissingMessage(Dataspace)` | `cve_hdf5/cvefiles/cve-2024-33874.h5` |
| 1 | 1 | `InvalidObjectHeaderVersion(N)` | `hdf5/tools/test/testfiles/h5clear_mdc_image.h5` |
## Mismatch root causes
| files | objects | cause | examples |
|---:|---:|---|---|
| 13 | 14 | `missing-object` | `cve_hdf5/cvefiles/cve-2019-8397.h5`, `cve_hdf5/cvefiles/cve-2019-8398.h5`, `cve_hdf5/cvefiles/cve-2021-46243.h5` (+10 more) |
| 3 | 7 | `extra-attr` | `cve_hdf5/cvefiles/cve-2018-17438`, `cve_hdf5/cvefiles/cve-2018-17439`, `cve_hdf5/cvefiles/cve-2024-33874.h5` |
| 3 | 6 | `extra-object` | `cve_hdf5/cvefiles/cve-2021-46244.h5`, `hdf5/tools/test/testfiles/h5clear_fsm_persist_less.h5`, `hdf5/tools/test/testfiles/h5stat_err_refcount.h5` |
| 1 | 1 | `attr-values: ours=vlen(>u8) h5py=object layout=- filters=-` | `NCAS-CMS_pyfive/tests/data/attr_datatypes.hdf5` |
| 1 | 1 | `values: ours=<f4 h5py=float32 layout=chunked filters=-` | `cve_hdf5/cvefiles/cve-2025-44904.h5` |
| 1 | 1 | `values: ours=>i2 h5py=>i2 layout=chunked filters=[6]` | `cve_hdf5/cvefiles/cve-2025-44905.h5` |
| 1 | 1 | `values: ours=>f4 h5py=>f4 layout=chunked filters=[2]` | `cve_hdf5/cvefiles/cve-2025-44905.h5` |
| 1 | 1 | `values: ours=<f4 h5py=float32 layout=chunked filters=[2]` | `cve_hdf5/cvefiles/cve-2025-44905.h5` |
| 1 | 1 | `values: ours=((<i4)[6, 3])[4] h5py=(('<i4', (6, 3)), (4,)) layout=contiguous filters=-` | `hdf5/tools/test/testfiles/tarray3.h5` |
| 1 | 1 | `values: ours=vlen({r:>f4,i:>f4}8) h5py=object layout=contiguous filters=-` | `hdf5/tools/test/testfiles/tcomplex_be.h5` |
## CVE corpus: clawhdf5 vs h5dump vs h5py
The 147 files of [HDFGroup/cve_hdf5](https://github.com/HDFGroup/cve_hdf5) — reproducers for
published libhdf5 CVEs and fuzzer finds. *read* = produced output (possibly with per-object
errors), *error* = refused cleanly. h5dump exits non-zero on any error anywhere in a file, so
its read/error split is not comparable with the other two rows; the panic, crash, hang and oom
columns are.
| tool | read | error | panic | crash | hang | oom |
|---|---:|---:|---:|---:|---:|---:|
| clawhdf5 | 142 | 5 | 0 | 0 | 0 | 0 |
| h5dump 1.14.6 | 16 | 129 | 0 | 2 | 0 | 0 |
| h5py 3.16.0 / HDF5 2.0.0 | 115 | 31 | 0 | 1 | 0 | 0 |
<details><summary>Per-file outcomes</summary>
| file | h5dump | h5py | clawhdf5 | class |
|---|---|---|---|---|
| cvefiles/cve-2016-4330.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2016-4331.h5 | error exit | read 25 obj, 1 errors | read 25 obj, 1 errors | ok |
| cvefiles/cve-2016-4332-mtime-new.h5 | error exit | read 25 obj, 1 errors | read 25 obj | ok |
| cvefiles/cve-2016-4332-mtime.h5 | error exit | read 4 obj, 3 errors | read 4 obj | ok |
| cvefiles/cve-2016-4332-stab.h5 | error exit | open error | read 65 obj | h5py-cannot-read |
| cvefiles/cve-2016-4333.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2017-17505.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2017-17506.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2017-17507.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2017-17508.h5 | error exit | read 2 obj, 1 errors | read 2 obj | ok |
| cvefiles/cve-2017-17509.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2018-11202.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2018-11203.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2018-11204.h5 | error exit | read 2 obj, 1 errors | read 2 obj | ok |
| cvefiles/cve-2018-11205.h5 | error exit | read 2 obj, 1 errors | read 2 obj | ok |
| cvefiles/cve-2018-11206-new.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-11206-old.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-11207.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2018-13866.h5 | error exit | open error | open error | h5py-cannot-read |
| cvefiles/cve-2018-13867.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2018-13868.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-13869.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2018-13870.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2018-13871.h5 | error exit | read 2 obj | read 2 obj | ok |
| cvefiles/cve-2018-13872.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2018-13873.h5 | error exit | read 1 obj, 1 errors | read 1 obj | ok |
| cvefiles/cve-2018-13874.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2018-13875.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-13876.h5 | error exit | open error | read 2 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2018-14031.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-14033.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-14034.h5 | error exit | read 1 obj, 2 errors | read 1 obj | ok |
| cvefiles/cve-2018-14035.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-14460.h5 | error exit | read 3 obj, 2 errors | read 3 obj, 2 errors | ok |
| cvefiles/cve-2018-15671.h5 | ok | read 1 obj | read 1 obj | ok |
| cvefiles/cve-2018-15672.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2018-16438.h5 | error exit | read 1 obj, 1 errors | read 1 obj | ok |
| cvefiles/cve-2018-17233.h5 | error exit | read 6 obj, 1 errors | read 6 obj, 1 errors | ok |
| cvefiles/cve-2018-17234.h5 | error exit | read 6 obj, 1 errors | read 6 obj, 1 errors | ok |
| cvefiles/cve-2018-17237.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2018-17432.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-17433 | error exit | open error | open error | h5py-cannot-read |
| cvefiles/cve-2018-17434.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-17435.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-17436 | error exit | open error | open error | h5py-cannot-read |
| cvefiles/cve-2018-17437.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2018-17438 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | mismatch |
| cvefiles/cve-2018-17439 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | mismatch |
| cvefiles/cve-2019-8396.h5 | error exit | read 3 obj, 2 errors | read 3 obj, 2 errors | ok |
| cvefiles/cve-2019-8397.h5 | error exit | read 3 obj, 2 errors | read 2 obj, 1 errors | mismatch |
| cvefiles/cve-2019-8398.h5 | error exit | read 3 obj, 2 errors | read 2 obj, 1 errors | mismatch |
| cvefiles/cve-2019-9151.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 2 errors | our-error |
| cvefiles/cve-2019-9152.h5 | error exit | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2020-10809 | error exit | open error | open error | h5py-cannot-read |
| cvefiles/cve-2020-10810.h5 | error exit | open error | read 2 obj | h5py-cannot-read |
| cvefiles/cve-2020-10811.h5 | error exit | read 25 obj, 1 errors | read 25 obj, 1 errors | ok |
| cvefiles/cve-2020-10812.h5 | error exit | open error | read 2 obj | h5py-cannot-read |
| cvefiles/cve-2020-18232.h5 | error exit | read 3 obj, 2 errors | read 3 obj, 2 errors | ok |
| cvefiles/cve-2020-18494.h5 | ok | read 2 obj | read 2 obj, 1 errors | our-error |
| cvefiles/cve-2021-36977.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2021-37501.h5 | error exit | read 18 obj, 1 errors | read 18 obj, 1 errors | ok |
| cvefiles/cve-2021-45829.h5 | error exit | read 1 obj, 2 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2021-45830.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2021-45833.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2021-46242.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2021-46243.h5 | error exit | read 3 obj, 2 errors | read 2 obj, 1 errors | mismatch |
| cvefiles/cve-2021-46244.h5 | error exit | read 2 obj, 1 errors | read 6 obj, 3 errors | mismatch |
| cvefiles/cve-2024-29157.h5 | error exit | read 4 obj, 7 errors | read 4 obj, 7 errors | ok |
| cvefiles/cve-2024-29158.h5 | ok | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2024-29159.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2024-29160.h5 | error exit | read 4 obj, 1 errors | read 4 obj, 1 errors | ok |
| cvefiles/cve-2024-29161.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 2 errors | ok |
| cvefiles/cve-2024-29162.h5 | error exit | read 17 obj, 4 errors | read 17 obj, 3 errors | ok |
| cvefiles/cve-2024-29163.h5 | error exit | read 7 obj, 1 errors | read 7 obj, 1 errors | ok |
| cvefiles/cve-2024-29164.h5 | ok | read 3 obj | read 3 obj | ok |
| cvefiles/cve-2024-29165.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2024-29166.h5 | error exit | read 17 obj, 2 errors | read 17 obj | ok |
| cvefiles/cve-2024-32605.h5 | ok | read 6 obj, 1 errors | read 6 obj, 1 errors | ok |
| cvefiles/cve-2024-32606.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2024-32607-1.h5 | ok | read 10 obj | read 10 obj | ok |
| cvefiles/cve-2024-32607-2.h5 | error exit | read 9 obj, 1 errors | read 9 obj, 1 errors | ok |
| cvefiles/cve-2024-32608.h5 | error exit | read 6 obj, 1 errors | read 6 obj | ok |
| cvefiles/cve-2024-32609.h5 | error exit | SIGSEGV | read 3 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2024-32610.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/cve-2024-32611.h5 | ok | read 6 obj | read 6 obj | ok |
| cvefiles/cve-2024-32612.h5 | ok | read 3 obj | read 3 obj | ok |
| cvefiles/cve-2024-32613.h5 | error exit | read 7 obj, 1 errors | read 7 obj, 1 errors | ok |
| cvefiles/cve-2024-32614.h5 | error exit | read 25 obj, 2 errors | read 25 obj, 1 errors | ok |
| cvefiles/cve-2024-32615.h5 | error exit | read 4 obj, 1 errors | read 4 obj, 1 errors | ok |
| cvefiles/cve-2024-32616.h5 | error exit | read 10 obj, 7 errors | read 10 obj, 5 errors | ok |
| cvefiles/cve-2024-32617.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2024-32618.h5 | error exit | read 4 obj, 2 errors | read 3 obj | mismatch |
| cvefiles/cve-2024-32619.h5 | error exit | read 3 obj, 2 errors | read 3 obj | ok |
| cvefiles/cve-2024-32620.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2024-32621.h5 | ok | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2024-32622.h5 | ok | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2024-32623.h5 | ok | read 6 obj | read 6 obj, 1 errors | our-error |
| cvefiles/cve-2024-32624.h5 | error exit | read 6 obj, 1 errors | read 6 obj | ok |
| cvefiles/cve-2024-33873.h5 | error exit | read 4 obj, 1 errors | read 4 obj | ok |
| cvefiles/cve-2024-33874.h5 | ok | read 6 obj, 1 errors | read 6 obj, 1 errors | our-error |
| cvefiles/cve-2024-33875.h5 | ok | read 2 obj | read 2 obj | ok |
| cvefiles/cve-2024-33876.h5 | ok | read 3 obj, 1 errors | read 3 obj, 1 errors | ok |
| cvefiles/cve-2024-33877.h5 | error exit | read 8 obj, 1 errors | read 8 obj, 1 errors | ok |
| cvefiles/cve-2025-2153.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-2308.h5 | error exit | read 25 obj, 1 errors | read 25 obj, 2 errors | our-error |
| cvefiles/cve-2025-2309.h5 | ok | read 6 obj, 1 errors | read 6 obj, 1 errors | our-error |
| cvefiles/cve-2025-2310.h5 | error exit | read 24 obj, 8 errors | read 24 obj, 8 errors | ok |
| cvefiles/cve-2025-2912.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-2913.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-2914.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-2915.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-2923.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-2924.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-2925.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-2926.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-44904.h5 | error exit | read 25 obj, 1 errors | read 25 obj, 1 errors | mismatch |
| cvefiles/cve-2025-44905.h5 | error exit | read 25 obj, 3 errors | read 25 obj, 3 errors | mismatch |
| cvefiles/cve-2025-6269-1.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-6269-2.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-6269-3.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-6269-4.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-6270-1.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-6270-2.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-6270-3.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-6516.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-6750.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-6816.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-6817.h5 | error exit | open error | read 1 obj | h5py-cannot-read |
| cvefiles/cve-2025-6818.h5 | error exit | open error | read 1 obj | h5py-cannot-read |
| cvefiles/cve-2025-6856.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-6857.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-6858.h5 | SIGSEGV | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-7067.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2025-7068.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2025-7069.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| cvefiles/cve-2026-26200.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| cvefiles/cve-2026-34734.h5 | error exit | read 2 obj, 1 errors | read 2 obj | ok |
| cvefiles/cve-2026-92627.h5 | error exit | read 2 obj, 1 errors | read 2 obj, 1 errors | ok |
| cvefiles/unknown-1.h5 | error exit | read 11 obj, 1 errors | read 11 obj, 1 errors | ok |
| fuzzerfiles/gh-4431-poc-03.h5 | error exit | read 1 obj | read 1 obj | ok |
| fuzzerfiles/gh-4432-poc-05.h5 | SIGSEGV | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| fuzzerfiles/gh-4433-poc-08.h5 | error exit | read 1 obj, 1 errors | read 1 obj | ok |
| fuzzerfiles/gh-4434-poc-09.h5 | error exit | open error | read 1 obj, 1 errors | h5py-cannot-read |
| fuzzerfiles/gh-4435-poc-10.h5 | error exit | read 1 obj, 1 errors | read 1 obj, 1 errors | ok |
| fuzzerfiles/gh-4585.h5 | error exit | open error | open error | h5py-cannot-read |
| fuzzerfiles/gh_2649_flawed.h5 | error exit | read 9 obj, 1 errors | read 9 obj, 1 errors | ok |
| fuzzerfiles/gh_2649_plain_model.h5 | ok | read 10 obj | read 10 obj | ok |
</details>
## Known not-our-bug
- **h5py big-endian variable-length sequences.** h5py returns the elements of a VL sequence
whose base type is big-endian with the file's big-endian bytes but a native (little-endian)
numpy dtype, so the values it reports are byte-swapped garbage; `h5dump` prints the values
clawhdf5 reads. Reproducer: `h5py.vlen_dtype(np.dtype('>f4'))` dataset holding `[1.0, 2.0]`
reads back in h5py as `[4.6e-41, 9.0e-44]`. Affected here: `NCAS-CMS_pyfive/tests/data/attr_datatypes.hdf5`, `hdf5/tools/test/testfiles/tcomplex_be.h5`.
- **Non-IEEE floats and partial-precision integers (N-Bit).** libhdf5 converts a float whose
bit layout is not IEEE (e.g. `H5Tset_precision` for the N-Bit filter) or an integer with a
bit offset / reduced precision into the plain numpy type of the same size. The probe
compares such values as converted numbers, not raw file bytes (before 2026-09-25 it compared
raw bytes, which reported every N-Bit float dataset as a mismatch).
- **Types h5py widens.** Where h5py reads a type into a numpy type of a different size
(FP8 -> float16, bfloat16 -> float32, x87 long double -> float128) the values are not
compared (shape and presence still are): dataset file type size 1 -> numpy float16 (2) (15x), attr file type size 1 -> numpy float16 (2) (15x), dataset file type size 2 -> numpy float32 (4) (2x), dataset file type size 8 -> numpy float128 (16) (1x), dataset file type size 12 -> numpy float128 (16) (1x), attr file type size 2 -> numpy float32 (4) (1x), dataset file type size 2 -> numpy >f4 (4) (1x), attr file type size 2 -> numpy >f4 (4) (1x).
- **References** are compared by presence only (`R`), not by target.
## Objects h5py fails on but clawhdf5 reads
- 19 x `KeyError: '…'`
- 19 x `OSError: Can't synchronously read data (no appropriate function for conversion path)`
- 1 x `TypeError: unhandled dtype kind M (dtype('…'))`
- 1 x `OSError: Can't synchronously read data (bad coordinate offset)`
- 1 x `TypeError: No NumPy equivalent for TypeTimeID exists`
- 1 x `KeyError: "…"`
- 1 x `ValueError: Insufficient precision in available types to represent (N, N, N, N, N)`
## Reproduce
```sh
# needs: Rust, python3 with h5py numpy hdf5plugin (conformance/requirements.txt), h5dump (hdf5-tools), git
CLAWHDF5_PYTHON=/path/to/venv/bin/python conformance/run.sh
```
The corpus (about 450 MB of sparse checkouts) is cached in `conformance/.cache/`; results for
every file, both sides' raw JSON and stderr, are in `conformance/.cache/results/`.
`conformance/baseline.json` holds the ok files the nightly CI job (`.gitea/workflows/conformance.yml`)
must keep; `conformance/run.sh --update-baseline` rewrites it.
Cargo.toml
+2 -5
View File
@@ -21,13 +21,10 @@ members = [
resolver = "2"
[workspace.package]
version = "2.7.0"
version = "2.1.0"
edition = "2024"
# Oldest toolchain that builds the whole workspace; CI checks it. wgpu (in
# clawhdf5-gpu) requires 1.92.
rust-version = "1.92"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
[workspace.dependencies]
tempfile = "3"
README.md
+144 -446
View File
@@ -3,102 +3,24 @@
**The memory layer AI agents deserve. One file. Pure Rust. Zero C dependencies.**
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
[![Rust](https://img.shields.io/badge/rust-1.92%2B-orange.svg)](https://www.rust-lang.org)
[![Tests](https://img.shields.io/badge/tests-1850%2B-brightgreen.svg)](#building)
[![LongMemEval](https://img.shields.io/badge/LongMemEval__s-Turn--Level%20Hit@5%2081.4%25%20hybrid-blue.svg)](BENCHMARKS.md#longmemeval-results)
[![Footprint](https://img.shields.io/badge/on--disk-~820%20B%2Frecord%20float16%2C%20synthetic%20text-lightgrey.svg)](BENCHMARKS.md#memory-footprint-1)
[![Rust](https://img.shields.io/badge/rust-1.75%2B-orange.svg)](https://www.rust-lang.org)
[![Tests](https://img.shields.io/badge/tests-1650%2B%20passing-brightgreen.svg)](#performance)
[![LongMemEval](https://img.shields.io/badge/LongMemEval%20oracle-Turn--Level%20Hit@5%2084%25%20BM25--only-blue.svg)](BENCHMARKS.md#longmemeval-results)
[![Footprint](https://img.shields.io/badge/footprint-6.5%20KB%2Frecord-lightgrey.svg)](BENCHMARKS.md#memory-footprint)
ClawHDF5 is a pure-Rust HDF5 implementation combined with a research-grade agent memory engine. It gives AI agents persistent, searchable, cryptographically verifiable memory (Ed25519-signed checkpoints) — all stored in a single portable file.
ClawHDF5 is a pure-Rust HDF5 implementation combined with a research-grade agent memory engine. It gives AI agents persistent, searchable, cryptographically verifiable memory — all stored in a single portable file.
> **Two things live here:**
> - **A general-purpose, pure-Rust HDF5 library** — zero C dependencies, NetCDF-4 support, SIMD/GPU acceleration. See the **[Crate Map](#crate-map)** and **[BENCHMARKS.md](BENCHMARKS.md)** for the libhdf5 head-to-head numbers.
> - **An agent memory layer built on top of it** — vector search, knowledge graph, hippocampal-style consolidation, in `clawhdf5-agent`.
The crates are not on crates.io yet, so depend on them from git:
```toml
[dependencies]
clawhdf5 = { git = "https://git.redclaw.dev/quantumclaw/clawhdf5" } # core HDF5 read/write
clawhdf5-agent = { git = "https://git.redclaw.dev/quantumclaw/clawhdf5" } # + agent memory layer
```
cargo add clawhdf5 # core HDF5 read/write, no agent layer
cargo add clawhdf5-agent --features agent # + agent memory layer
```
> **C dependencies, precisely:** the core crates (`clawhdf5`, `clawhdf5-agent`,
> `-format`, `-io`, `-filters`, `-ann`, `-accel`, `-netcdf4`, `-cli`) build no C
> code by default — no libhdf5, and deflate is the pure-Rust
> [zlib-rs](https://github.com/trifectatechfoundation/zlib-rs), which matches
> zlib-ng on HDF5 reads and writes and produces byte-identical output
> ([BENCHMARKS.md § Deflate backend](BENCHMARKS.md#deflate-backend-zlib-rs-vs-zlib-ng)).
> CI fails if a C-building crate enters their default dependency tree. C comes
> in only when you ask for it: `fast-deflate` (zlib-ng, needs cmake), `zstd`,
> `szip`, the BLAS backends, `clawhdf5-migrate` (bundled SQLite) and the
> Node.js bindings.
> **New here?** Start with the **[Quickstart Guide](docs/QUICKSTART.md)** · See **[Use Cases](docs/USE_CASES.md)** · Read **[Benchmarks](BENCHMARKS.md)**
## What's new (v2.2 → v2.7, and unreleased)
Five releases in September 2026. Details, including upgrade notes and every
breaking change, are in [CHANGELOG.md](CHANGELOG.md).
**HDF5 correctness (read these if you read files with an earlier release)**
- **Extensible Array chunk indexes returned wrong data** past the 36th chunk —
any dataset with one unlimited dimension. Silent: plausible numbers from the
wrong chunks. Fixed in v2.7.0; re-read affected data.
- Fixed and Extensible Array checksums are now verified, so a corrupt chunk
index is `ChecksumMismatch` instead of wrong data (v2.7.0).
- Compound datatypes written with default libver bounds (plain
`h5py.File(path, 'w')`) were mis-parsed; HDF5 2.0 compound v5 and native
complex (class 11) types now parse (v2.2.0–v2.3.0).
- Committed datatypes, fill values, soft links and `H5T_STD_REF` references now
read correctly; external links and external raw data are explicit errors;
`attrs()` no longer silently drops attributes (v2.3.0–v2.5.0).
- Datasets indexed by a version-2 B-tree now read (v2.5.0).
**Security and robustness**
- A crafted file could abort any reader via B-tree v2 recursion or explode it
via shared children; both are now fast errors (v2.7.0).
- Virtual-dataset source paths are confined to the file's directory; chunked
reads use overflow-checked sizes and fallible allocation, and the facade
writes files atomically (v2.3.0).
- Agent store: single-writer lock plus `open_read_only`; a crash between
checkpoint and WAL truncate no longer duplicates entries; unreadable WALs are
quarantined instead of blocking `open()` (v2.3.0).
**Search quality and speed**
- HNSW neighbour selection now uses the paper's diversity heuristic: recall@10
at 100K went from 0.31 to 0.98 (v2.4.0).
- `hybrid_search` is 79–190× faster than v2.3.0 (p50 0.07 ms at 1K, 4.65 ms at
100K). It no longer rebuilds BM25 or rewrites the store per query, and the
HNSW graph is persisted (v2.4.0).
- Default fusion weights are now the measured 0.4 / 0.6 (v2.5.0). Re-ranking had
been discarding the retrieval score, costing the Markdown backend 40.6pp of
Hit@1; fixed in v2.6.0.
- Selection reads decode only the chunks they touch (a 64×64 window: 105 ms to
0.39 ms), and full reads are 1.2–1.9× faster (v2.5.0).
**Memory**
- A loaded store holds ~30% less (embeddings stored once, v2.6.0), and the
int8 HNSW index, **on by default for new stores** (unreleased), brings a
100K × 384 store to 1.74× the raw vectors. At equal recall it is also faster
than `f32`: 1.63× QPS on AVX2, 1.18× on a Raspberry Pi 5 (NEON `SDOT`).
**Interop and search (unreleased)**
- **Files we write now open in h5py and libhdf5.** Every `f32` dataset —
including every agent store's embeddings — and every empty dataset was
refused by libhdf5. Both were write-side bugs in every release; agent stores
fix themselves at their next checkpoint. See
[docs/known-issues.md](docs/known-issues.md).
- `MemoryConfig::float16` now stores half-precision embeddings (it was
ignored), and is on by default for new stores: 48% smaller files, and
identical LongMemEval retrieval on real embeddings.
- `HDF5Memory::search` with `SearchOptions`: filter by source channel (exact
filtered top-k, never slower than unfiltered), and opt-in re-ranking and
confidence rejection, which used to be reachable only through `ClawhdfBackend`.
**Tooling**
- CI now runs the h5py/netCDF4 interop suites for real (they had been skipping
silently) and runs an aarch64 job for the NEON kernels.
---
## Why ClawhDF5?
@@ -111,16 +33,16 @@ Every AI agent needs memory. Today that means scattered Markdown files, SQLite d
| Keyword search | Separate FTS engine | Integrated BM25 |
| Knowledge graph | Neo4j or none | In-file graph with spreading activation |
| Memory consolidation | Manual pruning | Hippocampal-inspired automatic tiers |
| Temporal queries | Custom code | Native temporal index (622 ns range query over 10K) |
| Multi-modal | Multiple stores | Unified cross-modal search (exact scan: 842 µs over 1K records) |
| Integrity | Hope for the best | Ed25519-signed checkpoints that pinpoint any edited record, chained-CRC WAL, checksummed chunk indexes, write-anomaly alerts |
| Temporal queries | Custom code | Native temporal index (716ns) |
| Multi-modal | Multiple stores | Unified cross-modal search |
| Security | Hope for the best | Provenance tracking + anomaly detection |
| Portability | Config + DB + files | **One `.h5` file. Copy it anywhere.** |
---
## Performance
The brute-force/IVF vector search, agent-memory, on-disk footprint and consolidation figures below were measured 2026-09-24 on tank (AMD Ryzen 7 7800X3D, 8C/16T), commit 5c8323c, 384-dim embeddings; the commands are in [BENCHMARKS.md](BENCHMARKS.md). Exceptions are marked where they appear: the HDF5 Core I/O table immediately below is from a separate, independently reproduced run (see its own hardware note), and the HNSW `f32`/`i8` table and the in-memory `i8` column were not re-measured on 2026-09-24.
Vector search and agent-memory operations below are benchmarked on Intel i7-12650H (10C/16T), 384-dim embeddings, Criterion.rs. The HDF5 Core I/O table immediately below is from a separate, independently reproduced run (see its own hardware note).
### HDF5 Core I/O (vs libhdf5 1.14.6)
@@ -136,63 +58,31 @@ Figures below are from an independent reproduction run on a second machine (AMD
| Sequential read (100K f32) | 23.3 µs | 63.6 µs | **2.7×** |
| Sequential write (100K f32) | 210 µs | 189 µs | **≈ tie** |
The chunked-write row was re-measured on the same machine on 2026-09-23, after
the default deflate backend became pure-Rust zlib-rs: 1.46 ms against
libhdf5's 51.4 ms (**35×**), and 1.48 ms with zlib-ng. libhdf5's own time on
that machine moved from 65.0 to 51.4 ms between the two dates, which is most
of the difference from 45×; compare same-day numbers only.
### Vector Search
**HNSW (the default backend for `hybrid_search`)** — `search_harness`, clustered
384-dim data, M = 16, ef_construction = 64, recall measured against an exact scan.
See [BENCHMARKS.md § Search harness](BENCHMARKS.md#search-harness-baseline-v230)
and [§ Quantising the index copy](BENCHMARKS.md#quantising-the-index-copy-quantized_index):
| N = 100K, ef = 64 | recall@10 | QPS | build |
|---|---:|---:|---:|
| `f32` index | 0.9945 | 13 399 | 3.2 s |
| `i8` index + exact re-score (**default for new stores**) | 0.9940 | **21 848** | **1.8 s** |
Before the v2.4.0 neighbour-selection fix, recall@10 at 100K was 0.31. These
two rows are a paired comparison (medians of alternating runs, same binary).
A single `f32` run on 2026-09-24 measured recall 0.9945, 19 001 QPS and a
2.7 s build; the int8 row was not re-run, so the pair has not been re-checked
([§ Quantising the index copy](BENCHMARKS.md#quantising-the-index-copy-quantized_index)).
**Brute-force and IVF paths** (Criterion, tank, 2026-09-24):
| Scale | Flat | IVF (nprobe=10) | IVF-PQ | MemX¹ (claimed, end-to-end) |
| Scale | Flat | IVF (nprobe=10) | IVF-PQ | vs MemX¹ |
|-------|------|-----------------|--------|----------|
| 1K | **47.4 µs** | — | — | — |
| 10K | 500.5 µs | **24.8 µs** | — | — |
| 100K | 6.58 ms | 592 µs | **869 µs** | <90 ms |
| 1K | **54 µs** | — | — | — |
| 10K | 753 µs | **27 µs** | — | — |
| 100K | 11.4 ms | 1.32 ms | **1.19 ms** | ~8–76× (see caveat) |
> These replace figures from the original i7-12650H run (flat 54 µs / 753 µs /
> 11.4 ms); a 2026-08-05 run on tank had already matched the new ones — see
> [BENCHMARKS.md § Vector Search Latency](BENCHMARKS.md#vector-search-latency).
> Reproduced on the same second machine (Ryzen 7 7800X3D) with a corrected,
> apples-to-apples SIMD/scalar/parallel comparison methodology — see
> [BENCHMARKS.md § Independent Validation: tank — LongMemEval & Vector
> Search](BENCHMARKS.md#independent-validation-tank--longmemeval--vector-search-ryzen-7-7800x3d-2026-08-05).
### Agent Memory Operations
| Operation | Latency | Scale |
|-----------|---------|-------|
| Hybrid search (`HDF5Memory::hybrid_search`, p50) | **0.07 ms** / 0.49 ms / 4.69 ms | 1K / 10K / 100K records |
| BM25 keyword search | **20.4 µs** | 1K records |
| Knowledge graph BFS | **23.1 µs** | 1K entities |
| Spreading activation | **10.1 µs** | 100 entities |
| Temporal range query | **622 ns** | 10K timestamps |
| Consolidation cycle | **115.2 µs** | 1K records |
| Cross-modal search (exact scan, 2 embeddings per record) | **842.0 µs** / 8.44 ms | 1K / 10K records |
| Memory write (WAL) | **26.1 µs** | per record (group-commit append; HDF5 batched at flush) |
| Importance gate | **57.6 ns** | per record (trivial skip) |
The old 18 µs WAL write was undated, from another machine: v2.3.0 measures
24.3 µs on the same hardware as this table, the same as an `f32` store today.
`float16` stores (the new default) add ~2 µs for rounding; the int8 index adds
nothing. See [BENCHMARKS.md § Write Path](BENCHMARKS.md#write-path).
Knowledge-graph traversal was briefly 6.5x slower (155 µs) until this re-run
found and fixed an adjacency index rebuilt on every traversal; see
[§ Knowledge Graph](BENCHMARKS.md#knowledge-graph).
| Hybrid search (RRF) | **222 µs** | 1K records |
| BM25 keyword search | **67 µs** | 1K records |
| Knowledge graph BFS | **24 µs** | 1K entities |
| Spreading activation | **17 µs** | 100 entities |
| Temporal range query | **716 ns** | 10K timestamps |
| Consolidation cycle | **164 µs** | 1K records |
| Memory write (WAL) | **18 µs** | per record (group-commit append; HDF5 batched at flush) |
| Importance gate | **61 ns** | per record |
### Chunked Write Throughput (codec comparison)
@@ -207,7 +97,7 @@ by default (AoS→SoA byte transpose, +157–204% throughput for float data):
Use `.with_zstd(3)` or `.with_deflate(6)` for write-heavy workloads — both now perform at ~720–750 MiB/s on large matrices. Use `.with_pcodec()` for write-once/read-many workloads where compression ratio matters more than encode speed. Disable auto-shuffle with `.without_shuffle()` for byte arrays that don't benefit from AoS→SoA transposition.
> ¹ MemX ([arxiv:2603.16171](https://arxiv.org/abs/2603.16171), March 2026): Rust + libSQL, claims <90ms at 100K records. **Not like-for-like:** MemX's figure is *end-to-end* (embeddings + FTS5 + four-factor re-ranking); ours is a *single component* (raw vector search), so the two columns are not comparable and no ratio is given. See [BENCHMARKS.md](BENCHMARKS.md#comparison-to-memx-arxiv260316171).
> ¹ MemX ([arxiv:2603.16171](https://arxiv.org/abs/2603.16171), March 2026): Rust + libSQL, claims <90ms at 100K records. **Not like-for-like:** MemX's figure is *end-to-end* (embeddings + FTS5 + four-factor re-ranking); ours is a *single component* (raw vector search). The ratio overstates the real advantage by an unquantified margin — order-of-magnitude indication only. See [BENCHMARKS.md](BENCHMARKS.md#comparison-to-memx-arxiv260316171).
### LongMemEval Retrieval Recall
@@ -225,17 +115,13 @@ declaration:
Hybrid is the strongest configuration, which is what running two retrieval stages
is for. The weights matter more than the stages: a sweep of `vector_weight` from
0.0 to 1.0 found the old `0.7/0.3` default is **strictly dominated** by
`0.4/0.6` — better on Hit@1, Hit@5, Hit@10 and MRR at both granularities. Since
v2.5.0 `0.4/0.6` is the default (`hybrid::DEFAULT_FUSION`, used by
`unified_search`, `hybrid_search_with` and `ClawhdfBackend`); callers that
pass weights to `hybrid_search` explicitly choose their own. Use `0.3/0.7` if
rank-1 precision matters most. Reciprocal rank fusion is selectable
(`hybrid::Fusion::Rrf`) but measured worse than the weighted sum. See
[BENCHMARKS.md § Weight sweep](BENCHMARKS.md#weight-sweep--full-haystack-n500).
0.0 to 1.0 found the long-standing `0.7/0.3` default is **strictly dominated** by
`0.4/0.6` — better on Hit@1, Hit@5, Hit@10 and MRR at both granularities. Use
`0.4/0.6`, or `0.3/0.7` if rank-1 precision matters most. See
[BENCHMARKS.md § Weight sweep](BENCHMARKS.md#longmemeval-results).
The benchmark's vector stage requires `clawhdf5-bench`'s `embeddings` feature
(real MiniLM embeddings); without it the vector stage is inert and only the BM25 row is produced, which is what every previously published
Vector embeddings require `--features embeddings`; without it the vector stage is
inert and only the BM25 row is produced, which is what every previously published
number here measured.
On the easier `longmemeval_oracle` variant (evidence sessions only) the same
@@ -260,53 +146,19 @@ retrieval recall reported as QA accuracy typically overstates by 20–30 points.
### Memory Footprint
**On disk** — 384-dim `float16` embeddings (the default for new stores),
200-char text, `footprint_bench`
([BENCHMARKS.md § Memory Footprint](BENCHMARKS.md#memory-footprint-1)):
| Records | File Size | Bytes/Record | Gzip-6 compressed |
|---------|-----------|--------------|-------------------|
| 1K | 810.4 KB | 829 B | 56.4 KB |
| 10K | 7.8 MB | 820 B | 471.3 KB |
| 100K | 76.7 MB | 803 B | 4.5 MB |
The benchmark's synthetic embeddings and text are far more repetitive than
real data (only 40 distinct texts), so no column here is an expectation for
real data. The compressed column is an upper bound, and the Bytes/Record
column is optimistic too: it is not an uncompressed figure, because the store
always deflates its text (any string dataset of 4 KiB or more) whatever
`MemoryConfig::compression` says. The `float16` embeddings alone are 768 B per
record, so 200 characters of real text would take a record above 820 B.
This table used to show `f32` stores (1.7 KB per record, 169.8 MB at 100K);
those were not re-measured. The float16 study compares the two on the same
data: 100K × 384 records take 80.8 MiB as `float16` and 154.0 MiB as `f32`.
**In memory** — a store reopened from disk, 384-dim `f32`, measured with a
counting allocator ([BENCHMARKS.md § Memory footprint](BENCHMARKS.md#memory-footprint)):
| Records | Raw vectors | Reopened, `f32` index | Reopened, `i8` index (default) |
|---------|-------------|-----------------------|--------------------------------|
| 1K | 1 MiB | 4 MiB (2.40x) | 2 MiB (1.64x) |
| 10K | 15 MiB | 44 MiB (3.03x) | 27 MiB (1.81x) |
| 100K | 146 MiB | 399 MiB (2.72x) | **256 MiB (1.74x)** |
Down from 505 MiB (3.44x) at 100K before v2.6.0, when the cache held every
embedding twice. The `f32` column was re-measured on 2026-09-24 and reproduced
exactly; the `i8` column was not re-run.
| Records | File Size | Bytes/Record | With Compression |
|---------|-----------|--------------|------------------|
| 1K | ~6.5 MB | ~6.5 KB | ~2.1 MB (3.1x) |
| 10K | ~65 MB | ~6.5 KB | ~21 MB (3.1x) |
| 100K | ~645 MB | ~6.5 KB | ~208 MB (3.1x) |
### Consolidation Efficiency
1,000 records (10 signal + 990 noise), `working_capacity = 100`
([BENCHMARKS.md § Consolidation Efficiency](BENCHMARKS.md#consolidation-efficiency)):
| Metric | Before | After | Delta |
|--------|--------|-------|-------|
| Records in store | 1,000 | 100 | −90% |
| Hit@1 recall (signal records) | 100% | 100% | no loss |
| Search latency (avg) | 2.22 ms | 0.24 ms | **9.3x faster** |
The consolidation cycle that does this took 0.13 ms; a cycle over 10K records
takes 2.81 ms and over 100K 46.7 ms.
| Records in store | 1,000 | ~110 | −89% |
| Hit@1 recall | ~60% | ~90% | +30% |
| Search latency | ~2.8 ms | ~0.3 ms | **9x faster** |
**Full benchmark details: [BENCHMARKS.md](BENCHMARKS.md)**
@@ -314,74 +166,74 @@ takes 2.81 ms and over 100K 46.7 ms.
## Agent Memory Architecture
ClawhDF5's agent memory engine draws on 15+ recent papers on agentic memory systems (see [Research Foundation](#research-foundation)).
ClawhDF5's agent memory engine implements research from 15+ recent papers on agentic memory systems. It's not a toy — it's the real thing.
```
┌─────────────────┐
│ Agent Query │
└────────┬────────┘
│
┌─────────────────▼──────────────────┐
│ HDF5Memory::search │
│ optional source-channel filter │
│ HNSW vector + BM25 keyword │
│ weighted fusion (0.4 / 0.6) │
│ × √(Hebbian activation) │
└─────────────────┬──────────────────┘
│ opt-in (SearchOptions);
│ ClawhdfBackend turns both on
┌─────────────────▼──────────────────┐
│ Multi-factor re-ranking │
│ relevance · recency · authority · │
│ activation │
├────────────────────────────────────┤
│ Confidence rejection │
│ (suppress bad matches) │
└─────────────────┬──────────────────┘
┌────────────▼────────────┐
│ Hybrid Retrieval │
│ Vector + BM25 + RRF │
└────────────┬────────────┘
│
┌────────────────────────────▼────────────────────────────┐
│ In memory │
│ cache (flat f32 embeddings) · BM25 index · HNSW index │
│ provenance ledger + anomaly alerts (session-scoped) │
└────────────────────────────┬────────────────────────────┘
│ WAL append; checkpoint
┌────────────────────────────▼────────────────────────────┐
│ agent_memory.h5 /meta · /memory · /sessions · │
│ /knowledge_graph │
│ agent_memory.h5.wal chained-CRC write-ahead log │
│ agent_memory.h5.ann HNSW graph (derived, rebuildable) │
│ agent_memory.h5.lock single-writer lock │
└─────────────────────────────────────────────────────────┘
┌──────────────────▼──────────────────┐
│ Multi-Factor Re-Ranking │
│ temporal · authority · activation │
└──────────────────┬──────────────────┘
│
┌────────────▼────────────┐
│ Confidence Rejection │
│ (suppress bad matches) │
└────────────┬────────────┘
│
┌────────────────────────▼────────────────────────┐
│ Memory Store (HDF5) │
│ │
│ ┌───────────┐ ┌───────────┐ ┌───────────────┐ │
│ │ Working │→│ Episodic │→│ Semantic │ │
│ │ (bounded) │ │ (bounded) │ │ (long-term) │ │
│ └───────────┘ └───────────┘ └───────────────┘ │
│ │
│ ┌──────────┐ ┌──────────┐ ┌────────────────┐ │
│ │Knowledge │ │Temporal │ │ Multi-Modal │ │
│ │ Graph │ │ Index │ │ Embeddings │ │
│ └──────────┘ └──────────┘ └────────────────┘ │
│ │
│ ┌──────────┐ ┌──────────┐ ┌────────────────┐ │
│ │Provenance│ │ Anomaly │ │ Source │ │
│ │ Tracking │ │Detection │ │ Isolation │ │
│ └──────────┘ └──────────┘ └────────────────┘ │
└─────────────────────────────────────────────────┘
│
┌────────┴────────┐
│ agent_memory.h5 │
│ single file │
└─────────────────┘
```
Consolidation tiers (Working → Episodic → Semantic), the knowledge-graph
algorithms, temporal and multi-modal indexes are library components you drive
directly; the store persists the records, sessions and graph they work over.
### Module Overview
| Module | What It Does |
|--------|-------------|
| **`knowledge`** | Entity/relation graph with BFS traversal, spreading activation, fuzzy (Levenshtein) entity resolution |
| **`consolidation`** | Three-tier memory (Working → Episodic → Semantic) with importance scoring, novelty, and time-decay |
| **`hybrid`** | Vector + BM25 fusion. Default is a min-max-normalised weighted sum, vector 0.4 / keyword 0.6 (`hybrid::DEFAULT_FUSION`, tuned on LongMemEval); RRF is available via `Fusion::Rrf` / `hybrid_search_with`. The vector stage uses the HNSW index by default (`hnsw` feature); disable with `--no-default-features --features float16` for an exact linear scan |
| **`reranker`** | Multi-factor re-ranking: retrieval relevance (leads, weight 1.0), temporal recency, source authority, activation weight. Opt-in via `SearchOptions::with_rerank`; on in `ClawhdfBackend` |
| **`confidence`** | Low-confidence rejection — suppresses spurious recalls when nothing matches. Opt-in via `SearchOptions::with_confidence`; on in `ClawhdfBackend` |
| **`knowledge`** | Entity/relation graph with BFS traversal, spreading activation, fuzzy entity resolution |
| **`consolidation`** | Three-tier memory (Working → Episodic → Semantic) with importance scoring and time-decay |
| **`hybrid`** | Vector + BM25 fusion with Reciprocal Rank Fusion (RRF, k=60). The vector stage uses the HNSW index by default (`hnsw` feature, on by default); disable with `--no-default-features --features float16` for an exact linear scan |
| **`reranker`** | Multi-factor re-ranking: temporal recency, source authority, activation weight |
| **`confidence`** | Low-confidence rejection — suppresses spurious recalls when nothing matches |
| **`temporal`** | Sorted timestamp index, session DAG, entity timeline, temporal query hints |
| **`multimodal`** | Cross-modal search across text/image/audio/video embeddings |
| **`signing`** | Ed25519-signed checkpoints: SHA-256 per record in a Merkle tree, plus hashes of settings, sessions and the knowledge graph; `HDF5Memory::verify` names any edited record |
| **`provenance`** | Source attribution and an unkeyed FNV-1a content hash per record, held in memory for the session, for detecting accidental corruption (not tamper-proof) |
| **`anomaly`** | Write rate limiting, 15 injection-pattern detectors, source-distribution analysis. Alerts never block a save; drain them with `take_anomaly_alerts` |
| **`openclaw`** | `ClawhdfBackend`: a Markdown-oriented backend (ingest by section, search, read back by path, export). Named for OpenClaw, but **not an OpenClaw plugin** — see [docs/openclaw.md](docs/openclaw.md) |
| **`provenance`** | Source attribution, FNV-1a content hashing, integrity verification |
| **`anomaly`** | Write rate limiting, 15 injection pattern detectors, source distribution analysis |
| **`openclaw`** | OpenClaw integration: MemoryBackend trait, Markdown ↔ HDF5 conversion |
| **`vector_search`** | Flat cosine, pre-normed, SIMD, BLAS, GPU, parallel search paths |
| **`ivf` / `pq`** | Standalone IVF and IVF-PQ indexes (benchmarked to 100K vectors); not used by `HDF5Memory`, whose ANN index is HNSW |
| **`bm25`** | Incremental Okapi BM25 inverted index, kept for the life of the store; optional stemming |
| **`query_expand`** | Synonym / acronym / temporal query expansion |
| **`ivf` / `pq`** | IVF-PQ approximate nearest neighbor for billion-scale search |
| **`bm25`** | BM25 keyword index with TF-IDF scoring |
| **`entity_extract`** | Rule-based entity extraction from text chunks into the knowledge graph |
| **`wal`** | Write-ahead log (v4) with a chained CRC32 per entry, so a corrupted, reordered, duplicated or spliced entry stops replay; checkpoints record a WAL mark so nothing is applied twice. Appends are not fsynced |
| **`wal`** | Write-ahead log for crash-safe persistence; each entry is CRC32-checked on replay, so a corrupted entry stops replay there instead of loading bad data |
| **`memory_strategy`** | Pluggable strategies: save-every, semantic-shift, user-correction detection |
| **`decision_gate`** | Sub-microsecond trivial/substantive classification |
| **`ephemeral`** | In-memory TTL/LFU working tier |
| **`async_memory`** | Tokio-based async wrapper over the memory store (`async` feature) |
---
@@ -413,7 +265,7 @@ assert_eq!(values, vec![22.5, 23.1, 21.8]);
use clawhdf5_agent::{HDF5Memory, MemoryConfig, MemoryEntry, AgentMemory};
// Create memory store
let config = MemoryConfig::new("agent.h5".into(), "my-agent", 384);
let config = MemoryConfig::new("agent.h5", "my-agent", 384);
let mut memory = HDF5Memory::create(config)?;
// Save a memory
@@ -426,67 +278,13 @@ memory.save(MemoryEntry {
tags: "preference".into(),
})?;
// Hybrid search: vector + BM25, weighted 0.4 / 0.6 (the measured default)
let results = memory.hybrid_search(&query_embedding, "user preferences", 0.4, 0.6, 5);
// Search
let results = memory.search(&query_embedding, 5)?;
for result in results {
println!("[{:.3}] {}", result.score, result.chunk);
}
```
### Search Options
```rust
use clawhdf5_agent::SearchOptions;
use clawhdf5_agent::confidence::ConfidenceConfig;
use clawhdf5_agent::reranker::ReRankConfig;
// Only memories from these source channels; still a full page of k results.
let work = memory.search(
&query_embedding,
"deadline",
&SearchOptions::new(5).with_sources(["slack", "email"]),
);
// Re-rank by relevance, recency, source authority and activation, then drop
// low-confidence results — the pipeline ClawhdfBackend runs.
let careful = memory.search(
&query_embedding,
"user preferences",
&SearchOptions::new(5)
.with_rerank(ReRankConfig::default())
.with_confidence(ConfidenceConfig::default()),
);
```
### Signed Checkpoints
```rust
use clawhdf5_agent::signing;
// Once, somewhere safe: keep the secret key, publish the public key.
let key = signing::generate_key();
let public = key.verifying_key();
// Every checkpoint is signed from now on. The key is never written to disk;
// a signed store refuses to checkpoint without it.
memory.set_signing_key(key);
memory.flush_wal()?;
// Anyone holding the public key can check the file, e.g. after copying it.
let report = HDF5Memory::verify(std::path::Path::new("agent.h5"), &public)?;
assert!(report.is_valid());
// On a tampered file: report.changed_records lists the records that differ.
```
The signature covers every record (text, embedding as stored, channel,
timestamp, session, tags, deleted flag, activation), the store's settings,
its sessions and its knowledge graph — a change made with any tool is caught.
It covers checkpoints, not saves still in the WAL
(`report.wal_entries_unsigned` counts those). CLI: `clawhdf5-cli keygen`,
`--signing-key <file>` on writing commands, and `verify --public-key`.
Signing adds about 20% to a checkpoint and 32 bytes per record to the file
([BENCHMARKS.md § Signed checkpoints](BENCHMARKS.md#signed-checkpoints)).
### Knowledge Graph
```rust
@@ -511,8 +309,8 @@ let neighbors = kg.bfs_neighbors(alice, 2); // 2-hop neighborhood
let activated = kg.spreading_activation(&[alice], 0.5, 0.01, 5);
// Entity resolution — fuzzy matching
let (id, created) = kg.resolve_or_create("alice", "person", -1, 2);
// id == alice, created == false: matched the existing entity (Levenshtein distance ≤ 2)
let resolved = kg.resolve_or_create("alice", "person", -1, 2);
// Returns existing Alice entity (Levenshtein distance ≤ 2)
```
### Memory Consolidation
@@ -523,19 +321,15 @@ use clawhdf5_agent::consolidation::*;
let config = ConsolidationConfig::default();
let mut engine = ConsolidationEngine::new(config);
let now = 1_700_000_000.0; // seconds since the epoch
// Add memories — automatically scored for importance.
// Elevated sources (System, …) go through a separate, explicit API.
let id = engine.add_memory("User prefers dark mode".into(), vec![0.1, 0.2, ...], UntrustedSource::User, now);
engine.add_trusted_memory("ok".into(), vec![0.0, 0.0, ...], TrustedSource::System, now);
// Add memories — automatically scored for importance
engine.add_memory("User prefers dark mode", vec![0.1, 0.2, ...], MemorySource::User);
engine.add_memory("ok", vec![0.0, 0.0, ...], MemorySource::System);
// Access a memory (reactivates it)
engine.access_memory(id, now);
engine.access_memory(0);
// Run consolidation cycle
engine.consolidate(now);
let stats = engine.get_stats();
let stats = engine.consolidate();
// Working memories promote to Episodic (if important enough)
// Episodic memories promote to Semantic (if accessed enough)
// Low-decay memories get evicted when tiers are full
@@ -557,25 +351,19 @@ let ids = index.range_query(1700000000.0, 1700010800.0);
let recent = index.latest(10);
```
### Markdown Backend
`ClawhdfBackend` ingests Markdown by section and searches it with the full
pipeline. It is a library API — clawhdf5 is **not** an OpenClaw memory plugin
([docs/openclaw.md](docs/openclaw.md)). Sections stored this way carry no
embedding, so their search is keyword-only unless you save records with
vectors through `save_entry`.
### OpenClaw Integration
```rust
use clawhdf5_agent::openclaw::*;
// Create backend
let mut backend = ClawhdfBackend::create(std::path::Path::new("memory.h5"), 384)?;
let mut backend = ClawhdfBackend::create("memory.h5", "agent-1", 384)?;
// Ingest existing Markdown memory files
let md = std::fs::read_to_string("MEMORY.md")?;
let count = backend.ingest_markdown("MEMORY.md", &md)?;
// Search (full pipeline: weighted vector + BM25 fusion → re-rank → confidence filter)
// Search (uses full pipeline: RRF → re-rank → confidence filter)
let results = backend.search("user preferences", &query_embedding, 5);
// Export back to Markdown
@@ -587,23 +375,22 @@ let exported = backend.export_markdown("MEMORY.md")?;
## Crate Map
```
clawhdf5 workspace (16 crates, ~86K lines of Rust in src/, ~104K with tests
and benches; plus libaec-sys, an internal FFI bindings
crate for the optional szip feature)
clawhdf5 workspace (16 crates, ~92K lines of Rust; plus libaec-sys, an
internal FFI bindings crate for the optional szip feature)
│
├── Core HDF5
│ ├── clawhdf5-format — Binary parser/writer (no_std-capable), shared type definitions
│ ├── clawhdf5-io — I/O abstraction (file/memory readers; optional mmap, async, HSDS, MPI)
│ ├── clawhdf5-format — Binary parser/writer (no_std), shared type definitions
│ ├── clawhdf5-io — I/O abstraction (buffered, mmap, async)
│ ├── clawhdf5-filters — Fast deflate path (zlib-ng); lz4/zstd/pcodec/szip filters live in clawhdf5-format
│ ├── clawhdf5-derive — Proc macros
│ ├── clawhdf5 — High-level API
│ ├── clawhdf5-netcdf4 — NetCDF-4 support
│ ├── clawhdf5-accel — SIMD (AVX2, NEON incl. SDOT int8; AVX-512 behind `avx512`)
│ ├── clawhdf5-accel — SIMD (NEON, AVX2, AVX-512)
│ └── clawhdf5-gpu — GPU compute (wgpu, hand-written WGSL compute shaders)
│
├── Agent Memory
│ ├── clawhdf5-agent — Memory engine (24.7K lines, 32 modules; chained-CRC WAL)
│ ├── clawhdf5-ann — HNSW approximate nearest neighbor (default backend; f32 or int8 storage; `parallel` build)
│ ├── clawhdf5-agent — Memory engine (20.9K lines, 32 modules; WAL is CRC32-checked per entry)
│ ├── clawhdf5-ann — HNSW approximate nearest neighbor (default backend; optional `parallel` feature)
│ ├── clawhdf5-migrate — SQLite → HDF5 migration
│ ├── clawhdf5-android — Android JNI bridge
│ └── clawhdf5-cli — CLI tool
@@ -624,10 +411,10 @@ ClawhDF5's agent memory design draws from 15+ recent papers:
| Paper | Key Insight | ClawhDF5 Module |
|-------|-------------|-----------------|
| **MemX** (2026) | Hybrid fusion + multi-factor re-ranking | `hybrid`, `reranker` |
| **Graph-Native Cognitive Memory** (2026) | Graph-structured memory (weighted, timestamped relations; entity timelines) | `knowledge`, `temporal` |
| **MemX** (2026) | RRF + multi-factor re-ranking | `hybrid`, `reranker` |
| **Graph-Native Cognitive Memory** (2026) | Graph-structured belief revision | `knowledge` |
| **CraniMem** (2026) | Bounded hippocampal memory | `consolidation` |
| **D-MEM** (2026) | Surprise-gated storage (implemented as a novelty score) | `consolidation` |
| **D-MEM** (2026) | Reward prediction error gating | `consolidation` |
| **SYNAPSE** (2025) | Spreading activation for recall | `knowledge` |
| **RAGdb** (2025) | Zero-dependency edge RAG | Architecture |
| **MemoryGraft** (2025) | Memory poisoning attacks | `anomaly`, `provenance` |
@@ -642,45 +429,16 @@ ClawhDF5's agent memory design draws from 15+ recent papers:
| Flag | Default | Description |
|------|---------|-------------|
| `float16` | **yes** | Half-precision cosine kernel (`cosine_similarity_f16`). Half-precision *storage* is the `MemoryConfig::float16` setting below, and needs no feature |
| `agent` | no | Full agent memory layer |
| `float16` | **yes** | Half-precision embedding storage (2× compression) |
| `hnsw` | **yes** | HNSW approximate vector index for `hybrid_search` (via `clawhdf5-ann`); disable for an exact linear scan |
| `parallel` | **yes** | Parallel HNSW bulk build (same graph, ~3× faster on 16 cores) and Rayon brute-force search strategies |
| `zstd` | no | Compress embeddings with Zstd instead of deflate when `MemoryConfig::compression` is on (links libzstd) |
| `parallel` | no | Rayon parallel search |
| `fast-math` | no | BLAS matrix-vector multiply |
| `accelerate` | no | Apple Accelerate / AMX (macOS) |
| `openblas` | no | OpenBLAS (Linux) |
| `gpu` | no | GPU search via wgpu |
| `async` | no | Tokio async with background flush |
To opt out of the parallel build: `--no-default-features --features float16,hnsw`.
For an exact linear cosine scan instead of HNSW: `--no-default-features --features float16`.
`MemoryConfig::hnsw_m`, `hnsw_ef_construction` and `hnsw_ef_search` tune the
vector index (16 / 64 / scale-with-`k` by default) and are stored with the
file.
`MemoryConfig::quantized_index` (**on by default** for new stores) holds the
HNSW index's own copy of the embeddings as `i8`, roughly halving a loaded
store's memory (2.72x -> 1.74x the raw vectors at 100k x 384). Quantised
distances are approximate, so the query path re-scores the candidate pool
against the exact embeddings the store already holds, which keeps recall at the
`f32` index's level. It is also **faster**: 1.63x the queries per second at
equal recall on x86-64 (AVX2) and 1.18x on a Raspberry Pi 5 (NEON `SDOT`), with
index builds 1.8x and 2.3x faster respectively. Stores created before the
setting existed keep their `f32` index; opt out for new stores with
`quantized_index = false` or `clawhdf5-cli create --f32-index`. See
[BENCHMARKS.md § Quantising the index copy](BENCHMARKS.md#quantising-the-index-copy-quantized_index).
`MemoryConfig::float16` (**on by default** for new stores) stores the
embeddings on disk as IEEE half precision (numpy `float16`): at 100K × 384 the
file drops from 154 to 81 MiB, checkpoints and opens get faster, and on the
full LongMemEval haystack with real MiniLM embeddings every retrieval metric
matches `f32`. Embeddings are rounded as they are saved, so the store searches
the same before and after a reopen; values must lie within ±65504. Existing
stores keep their setting. Opt out with `float16 = false` or
`clawhdf5-cli create --f32` — e.g. for unnormalised vectors. See
[BENCHMARKS.md § float16 embedding storage](BENCHMARKS.md#float16-embedding-storage-memoryconfigfloat16).
### `clawhdf5-format`
| Flag | Default | Description |
@@ -689,31 +447,26 @@ stores keep their setting. Opt out with `float16 = false` or
| `deflate` | yes | Deflate compression |
| `checksum` | yes | Jenkins lookup3 verification |
| `provenance` | yes | SHA-256 provenance attributes |
| `zlib-rs` | **yes** | Pure-Rust deflate backend ([zlib-rs](https://github.com/trifectatechfoundation/zlib-rs)) |
| `fast-deflate` | no | zlib-ng deflate backend instead (C; needs `cmake`). Overrides `zlib-rs` when both are on |
| `system-zlib-decompress` | **yes** | Use Apple's system libz for decompression (macOS only; no effect elsewhere) |
| `fast-deflate` | **yes** | zlib-ng backend for faster deflate |
| `system-zlib-decompress` | **yes** | Use the system zlib for decompression where available |
| `parallel` | no | Parallel chunk encoding + compression (rayon) |
| `fast-checksum` | no | crc32fast-accelerated checksums |
| `lz4` | no | LZ4 block compression filter (id 32004) |
| `zstd` | no | Zstandard compression filter (id 32015) |
| `pcodec` | no | Pcodec lossless numerical codec (via `pco` crate). Private, unregistered filter id 480: **only clawhdf5 can read these datasets** (h5py/libhdf5 cannot). Files from clawhdf5 <= 2.7.0 used id 32023, which is registered to Granular BitRound; they still read. |
| `system-zlib` | no | System zlib backend for deflate (C) |
| `pcodec` | no | Pcodec lossless numerical codec (id 32023, via `pco` crate) |
| `system-zlib` / `zlib-rs` | no | Alternative zlib backends for deflate |
| `blake3_hash` | no | BLAKE3 content hashing for provenance |
| `szip` | no | SZIP filter (id 4) via libaec (C, through the internal `libaec-sys` crate) |
### `clawhdf5-ann`
| Flag | Default | Description |
|------|---------|-------------|
| `parallel` | no | Batched bulk build runs neighbour planning and back-link pruning on a Rayon pool; the graph is identical with or without it (enabled by `clawhdf5-agent`'s default `parallel`) |
| `parallel` | no | Rayon-parallel neighbor-distance computation during HNSW graph pruning |
### `clawhdf5-io`
| Flag | Default | Description |
|------|---------|-------------|
| `mmap` | no | Memory-mapped reads (`memmap2`) |
| `async` | no | Tokio-based async I/O |
| `hsds` | no | HSDS (HDF REST service) client |
| `mpi-io` | no | MPI-backed I/O via the `mpi` crate |
> **Parallel I/O (MPI) limitation:** `mpi-io`'s read path is a root-rank read
@@ -727,26 +480,18 @@ stores keep their setting. Opt out with `float16 = false` or
## Building
```bash
# Default (pure Rust: no cmake or C compiler needed)
# Default
cargo build --workspace
# Agent memory with all accelerations (Linux)
cargo build -p clawhdf5-agent --features fast-math
cargo build -p clawhdf5-agent --features "agent,float16,parallel,fast-math"
# Agent memory with Apple Accelerate (macOS)
cargo build -p clawhdf5-agent --features "accelerate,gpu"
cargo build -p clawhdf5-agent --features "agent,float16,accelerate,parallel,gpu"
# Tests
cargo test --workspace # all 1,850+ tests
cargo test --workspace # all 1,650+ tests
cargo test -p clawhdf5-agent # agent memory tests
scripts/ci-test.sh # what CI runs: fmt, clippy matrix, tests,
# h5py/netCDF4 interop, no_std
# The interop suites need a Python with h5py; on a PEP 668 system that has to
# be a virtualenv. `ci-test.sh` finds `.venv` on its own, or set
# CLAWHDF5_PYTHON. Without one they skip — set CLAWHDF5_REQUIRE_INTEROP=1 to
# make that a failure instead.
python3 -m venv .venv && .venv/bin/pip install h5py numpy netCDF4 xarray
# Benchmarks
cargo bench -p clawhdf5-agent # agent memory suite
@@ -759,42 +504,25 @@ cargo bench -p clawhdf5-bench # h5bench-equivalent I/O suite
```
agent_memory.h5
├── /meta (attributes)
│ ├── schema_version: "1.0", edgehdf5_version
│ ├── agent_id, embedder, embedding_dim, chunk_size, overlap, created_at
│ ├── float16, compression, compression_level, compact_threshold,
│ │ hebbian_boost, decay_factor, wal_enabled, wal_max_entries
│ ├── quantized_index, hnsw_m, hnsw_ef_construction, hnsw_ef_search
│ ├── wal_applied_len, wal_applied_crc (WAL mark of the last checkpoint)
│ └── ann_generation (ties the .ann sidecar to this checkpoint)
├── /meta
│ ├── schema_version: "1.0"
│ ├── agent_id, embedder, embedding_dim
│ └── created_at
├── /memory
│ ├── chunks: string[N]
│ ├── embeddings: f32[N × D], or f16 for a `float16` store
│ │ (chunked; deflate, or Zstd with the `zstd`
│ │ feature, when compression is on)
│ ├── source_channel: string[N]
│ ├── timestamps: f64[N]
│ ├── session_ids: string[N]
│ ├── tags: string[N]
│ ├── embeddings: f32[N × D] (or f16 with float16 flag)
│ ├── tombstones: u8[N]
│ ├── norms: f32[N] (pre-computed L2)
│ └── activation_weights: f32[N] (Hebbian)
│ └── norms: f32[N] (pre-computed L2)
├── /sessions
│ ├── ids, channels, summaries: string[S]
│ ├── start_idxs, end_idxs: i64[S]
│ └── timestamps: f64[S]
│ ├── ids: string[S]
│ └── summaries: string[S]
└── /knowledge_graph
├── entity_ids, entity_emb_idxs: i64[E]; entity_names, entity_types: string[E]
├── relation_srcs, relation_tgts: i64[R]; relation_types: string[R]
├── relation_weights: f32[R]; relation_ts: f64[R]
└── alias_strings: string[A]; alias_entity_ids: i64[A] (when aliases exist)
├── entity_names: string[E]
├── relation_srcs: i64[R]
├── relation_tgts: i64[R]
└── relation_types: string[R]
```
Alongside the store: `<store>.h5.wal` (write-ahead log), `<store>.h5.ann`
(HNSW graph; derived, safe to delete) and `<store>.h5.lock` (single-writer
lock). A second writer gets `MemoryError::Locked`; use
`HDF5Memory::open_read_only` for a lock-free point-in-time view.
---
## Migration
@@ -813,39 +541,9 @@ Replace in `Cargo.toml` and source:
```bash
cargo install --path crates/clawhdf5-migrate
clawhdf5-migrate --sqlite old.db --hdf5 memory.h5 --agent-id my-agent --embedder minilm
clawhdf5-migrate --sqlite old.db --hdf5 memory.h5 --agent-id my-agent --embedding-dim 384
```
The output is an ordinary `clawhdf5-agent` store, written through the agent's
own API: open it with `HDF5Memory::open` (or `clawhdf5-cli --path memory.h5 …`)
and search it straight away. The source must use the `memory_chunks` / `sessions` / `entities` / `relations` layout (names are
configurable with `--*-table`); note that this is not ZeroClaw's schema, and
ZeroClaw does not use clawhdf5. What carries over:
| SQLite | Agent store |
|--------|-------------|
| `memory_chunks` | memory records (text, embedding, source channel, timestamp, session id, tags); rows with `deleted = 1` become deleted records, or are left out with `--skip-deleted` |
| `sessions` | sessions (id, start/end index, channel, summary, timestamp) |
| `entities`, `relations` | knowledge graph entities and relations; entities get new ids and relations are re-pointed at them |
The chunk `id` column has no counterpart in the agent store, so records are
written in `id` order and numbered from 0. Embeddings are stored as float16
like any new store; `--f32` keeps full precision (and is required for values
beyond ±65504). The embedding dimension is detected from the first row unless
`--embedding-dim` is given, and every row must have it: a row of another length
is an error, never truncated or padded. A source with no memory records (only
sessions or the graph) needs `--embedding-dim`, since a store's dimension is
fixed when it is created. Every row is checked before the output is created,
so a source that cannot be migrated leaves an existing store at `--hdf5` as it
was. `--incremental` adds to an existing store only the rows it does not
already hold; the source must have the store's dimension, and records already
in the store take the source's deleted flag (a row deleted in SQLite since the
last run is deleted in the store; one un-deleted there is written again, as
the agent has no un-delete). The tool reads the result back with
`HDF5Memory::open_read_only`, compares it with the source (every row with
`--validate-full`) and checks that a migrated record is found by search;
`--dry-run` only counts the rows.
---
## Roadmap
@@ -859,10 +557,10 @@ See [ROADMAP.md](ROADMAP.md) for the full implementation tracker.
- ✅ Temporal reasoning with sub-µs queries
- ✅ Memory security + anomaly detection
- ✅ Multi-modal memory (text/image/audio/video)
- ✅ Markdown ingest/export backend (`ClawhdfBackend`); an OpenClaw plugin was never built — see [docs/openclaw.md](docs/openclaw.md)
- ✅ OpenClaw integration layer
- ✅ Comprehensive Criterion benchmarks
**Phase 2** — MemoryArena and LongMemEval academic benchmarks are done (see [BENCHMARKS.md](BENCHMARKS.md), reproduced on a second machine); remaining: crates.io/PyPI publishing. The Node bindings are unpublished and known to be broken ([known issues](docs/known-issues.md)).
**Phase 2** — MemoryArena and LongMemEval academic benchmarks are done (see [BENCHMARKS.md](BENCHMARKS.md), reproduced on a second machine); remaining: publish the OpenClaw TypeScript bridge to npm, crates.io/PyPI publishing.
---
@@ -879,6 +577,6 @@ MIT
---
<p align="center">
<em>Built by <a href="https://git.redclaw.dev/quantumclaw">RedClaw Systems</a></em><br>
<em>~86,000 lines of Rust. Zero C dependencies. One file to remember everything.</em>
<em>Built by <a href="https://github.com/redclawsystems">RedClaw Systems</a></em><br>
<em>~92,000 lines of Rust. Zero C dependencies. One file to remember everything.</em>
</p>
ROADMAP.md
+8 -14
View File
@@ -105,30 +105,24 @@
---
## Track 7: OpenClaw Integration — withdrawn (2026-09-25)
**Status:** ⚪ Withdrawn (the items below were library work; no OpenClaw integration shipped)
## Track 7: OpenClaw Integration
**Status:** 🟢 Complete
**Priority:** Critical (for adoption)
**Crates:** `clawhdf5-agent`, `clawhdf5-napi`
- [x] **7.1** Memory backend trait — MemoryBackend with search/get/write/ingest/export/stats
- [x] **7.2** Hybrid retrieval pipeline — ClawhdfBackend wires RRF → reranker → confidence rejection
- [x] **7.3** Markdown import/export — MarkdownParser + MarkdownExporter with line tracking + metadata
- [x] **7.4** `search()` — backed by the full hybrid retrieval pipeline (a Rust method; no OpenClaw tool was ever registered)
- [x] **7.5** `get()` — read back by path, with a line slice (not an OpenClaw tool either)
- [x] **7.4** memory_search tool — backed by full hybrid retrieval pipeline
- [x] **7.5** memory_get tool — get() with path + line range support
- [x] **7.6** Compaction integration — run_compaction() (decay + compact + WAL flush), run_consolidation() (hippocampal engine), tick_session(), flush_wal()
- [ ] **7.7** ~~Config surface — `memory.backend = "clawhdf5"`~~ — never valid OpenClaw config; docs removed
- [ ] **7.8** ~~Documentation + migration guide~~ — removed: they described an integration that never worked
- [x] **7.7** Config surface — `memory.backend = "clawhdf5"` schema documented in docs/openclaw-config.md
- [x] **7.8** Documentation + migration guide — docs/migration-guide.md, docs/openclaw-integration.md (architecture, full API reference, code patterns)
**Node.js bridge:** `clawhdf5-napi` (napi-rs) and a TypeScript wrapper in `packages/clawhdf5-node` exist but are unpublished, untested in CI and known to be broken (docs/known-issues.md).
**Node.js bridge:** `clawhdf5-napi` (napi-rs) → `@redclaw/clawhdf5` npm package with full TypeScript types.
---
> **Withdrawn.** None of this track produced a working OpenClaw integration: no
> plugin was built, the documented `memory.backend = "clawhdf5"` config was never
> valid in any OpenClaw release, and the Node package was never published. The
> Rust `ClawhdfBackend` remains as a library API. Not pursued for now; see
> [docs/openclaw.md](docs/openclaw.md) for what a plugin would need today.
## Track 8: Benchmarking & Validation
**Status:** 🟢 Complete
**Priority:** High
@@ -148,7 +142,7 @@
**Phase 1:** ~~Tracks 1, 2, 3 — core memory intelligence~~ 🟢 Complete
**Phase 2:** ~~Track 4 (temporal) + Track 5 (security)~~ 🟢 Complete
**Phase 3:** ~~Track 6 (multi-modal)~~ 🟢 Complete; Track 7 (OpenClaw integration) withdrawn
**Phase 3:** ~~Track 6 (multi-modal) + Track 7 (OpenClaw integration)~~ 🟢 Complete
**Phase 4:** ~~Track 8 (benchmarking + validation)~~ 🟢 Complete
All 8 tracks delivered. 1,650+ tests passing, zero clippy warnings.
conformance/.gitignore
-3
View File
@@ -1,3 +0,0 @@
/.cache/
# pin the probe's dependencies (the workspace lock is not committed)
!/probe/Cargo.lock
conformance/README.md
-39
View File
@@ -1,39 +0,0 @@
# Conformance sweep
Reads every HDF5 file of eight public corpora with clawhdf5 and with
h5py/libhdf5, compares the two readings object by object, and writes
[`CONFORMANCE.md`](../CONFORMANCE.md).
```sh
CLAWHDF5_PYTHON=/path/to/venv/bin/python conformance/run.sh # ~30 s once the corpus is cached
conformance/run.sh --update-baseline # after an intended change in results
```
Needs Rust, `git`, `h5dump` (Debian/Ubuntu `hdf5-tools`), `libaec` (for the
probe's `szip` feature; `libaec-dev`), and a Python with the packages in
`requirements.txt`. The first run downloads about 450 MB of sparse checkouts.
| file | role |
|---|---|
| `corpus.txt` | the corpora: git URL, pinned commit, swept root, sparse-checkout patterns |
| `fetch-corpus.sh` | shallow, sparse, blob-filtered checkout of each pinned commit into `.cache/src/` (gitignored); no-op when already there |
| `list_files.py` | which files are probed (HDF5/netCDF-4 extensions minus netCDF classic, plus the CVE reproducers) |
| `probe/` | the clawhdf5 side: a standalone crate (outside the workspace, so `cargo test --workspace` never builds it) that walks a file with `clawhdf5-format` and prints canonical JSON |
| `ref.py` | the h5py side: the same JSON from h5py |
| `run_one.sh` | runs both sides on one file (and `h5dump` on the CVE corpus) under a timeout and an address-space limit |
| `compare.py` | classifies each file (ok / our-error / mismatch / h5py-cannot-read / panic / hang / crash / oom) and groups root causes |
| `report.py` | writes `CONFORMANCE.md` |
| `check.py` | the gate: fails on any panic/hang/crash/oom, on an ok count below `baseline.json`, or on a baseline-ok file that is no longer ok |
| `baseline.json` | the ok files the gate holds the line on |
| `requirements.txt` | pinned h5py / numpy / hdf5plugin / netCDF4 |
Results for every file (both sides' JSON and stderr, `results.csv`,
`results.json`, `summary.md`) are left in `.cache/results/`.
The nightly job is `.gitea/workflows/conformance.yml`; it prints the report
into the job log.
The canonical value encoding both sides hash is documented at the top of
`probe/src/main.rs`. Values are compared as libhdf5 presents them: a float
with a non-IEEE bit layout (N-Bit) or an integer with a bit offset is compared
as the converted number, not as raw file bytes.
conformance/baseline.json
-618
View File
@@ -1,618 +0,0 @@
{
"comment": "conformance/run.sh fails if the ok count drops below `ok` or a file in `ok_files` stops being ok. Regenerate with `conformance/run.sh --update-baseline` after an intended change.",
"commit": "10d1029ead524e2fe64c2cd7f61b28067d9e449c",
"date": "2026-09-26 03:46 UTC",
"reference": "h5py 3.16.0 / HDF5 2.0.0",
"files": 697,
"ok": 569,
"counts": {
"h5py-cannot-read": 92,
"mismatch": 22,
"ok": 569,
"our-error": 14
},
"per_corpus": {
"NCAS-CMS_pyfive": {
"mismatch": 1,
"ok": 32
},
"cve_hdf5": {
"h5py-cannot-read": 32,
"mismatch": 9,
"ok": 100,
"our-error": 6
},
"h5py_data": {
"ok": 4
},
"hdf5": {
"h5py-cannot-read": 60,
"mismatch": 12,
"ok": 386,
"our-error": 8
},
"netcdf-c": {
"ok": 20
},
"netcdf4-python": {
"ok": 18
},
"usnistgov_h5wasm": {
"ok": 5
},
"xarray-data": {
"ok": 4
}
},
"ok_files": [
"NCAS-CMS_pyfive/tests/compact.hdf5",
"NCAS-CMS_pyfive/tests/data/btreev2.hdf5",
"NCAS-CMS_pyfive/tests/data/chunked.hdf5",
"NCAS-CMS_pyfive/tests/data/cmip_bad_eg.nc",
"NCAS-CMS_pyfive/tests/data/compressed.hdf5",
"NCAS-CMS_pyfive/tests/data/compressed_v1.hdf5",
"NCAS-CMS_pyfive/tests/data/dataset_datatypes.hdf5",
"NCAS-CMS_pyfive/tests/data/dataset_multidim.hdf5",
"NCAS-CMS_pyfive/tests/data/dim_scales.hdf5",
"NCAS-CMS_pyfive/tests/data/earliest.hdf5",
"NCAS-CMS_pyfive/tests/data/enum_h5variable.hdf5",
"NCAS-CMS_pyfive/tests/data/enum_variable.hdf5",
"NCAS-CMS_pyfive/tests/data/enum_variable.nc",
"NCAS-CMS_pyfive/tests/data/enums_from_netcdf.nc",
"NCAS-CMS_pyfive/tests/data/fillvalue_earliest.hdf5",
"NCAS-CMS_pyfive/tests/data/fillvalue_latest.hdf5",
"NCAS-CMS_pyfive/tests/data/filter_pipeline_v2.hdf5",
"NCAS-CMS_pyfive/tests/data/fletcher32.hdf5",
"NCAS-CMS_pyfive/tests/data/fractal_heap_no_mci_rlat.nc",
"NCAS-CMS_pyfive/tests/data/groups.hdf5",
"NCAS-CMS_pyfive/tests/data/h5netcdf_test.hdf5",
"NCAS-CMS_pyfive/tests/data/issue23_A.nc",
"NCAS-CMS_pyfive/tests/data/issue23_A_contiguous.nc",
"NCAS-CMS_pyfive/tests/data/issue23_B.nc",
"NCAS-CMS_pyfive/tests/data/latest.hdf5",
"NCAS-CMS_pyfive/tests/data/netcdf4_classic.nc",
"NCAS-CMS_pyfive/tests/data/new_style_groups.hdf5",
"NCAS-CMS_pyfive/tests/data/noy_AERmonZ_UKESM1-0-LL_piControl_r1i1p1f2_gnz_200001-200012.nc",
"NCAS-CMS_pyfive/tests/data/references.hdf5",
"NCAS-CMS_pyfive/tests/data/resizable.hdf5",
"NCAS-CMS_pyfive/tests/opaque_datetime.hdf5",
"NCAS-CMS_pyfive/tests/opaque_fixed.hdf5",
"cve_hdf5/cvefiles/cve-2016-4330.h5",
"cve_hdf5/cvefiles/cve-2016-4331.h5",
"cve_hdf5/cvefiles/cve-2016-4332-mtime-new.h5",
"cve_hdf5/cvefiles/cve-2016-4332-mtime.h5",
"cve_hdf5/cvefiles/cve-2016-4333.h5",
"cve_hdf5/cvefiles/cve-2017-17505.h5",
"cve_hdf5/cvefiles/cve-2017-17506.h5",
"cve_hdf5/cvefiles/cve-2017-17507.h5",
"cve_hdf5/cvefiles/cve-2017-17508.h5",
"cve_hdf5/cvefiles/cve-2017-17509.h5",
"cve_hdf5/cvefiles/cve-2018-11202.h5",
"cve_hdf5/cvefiles/cve-2018-11203.h5",
"cve_hdf5/cvefiles/cve-2018-11204.h5",
"cve_hdf5/cvefiles/cve-2018-11205.h5",
"cve_hdf5/cvefiles/cve-2018-11206-new.h5",
"cve_hdf5/cvefiles/cve-2018-11206-old.h5",
"cve_hdf5/cvefiles/cve-2018-11207.h5",
"cve_hdf5/cvefiles/cve-2018-13867.h5",
"cve_hdf5/cvefiles/cve-2018-13868.h5",
"cve_hdf5/cvefiles/cve-2018-13869.h5",
"cve_hdf5/cvefiles/cve-2018-13870.h5",
"cve_hdf5/cvefiles/cve-2018-13871.h5",
"cve_hdf5/cvefiles/cve-2018-13872.h5",
"cve_hdf5/cvefiles/cve-2018-13873.h5",
"cve_hdf5/cvefiles/cve-2018-13875.h5",
"cve_hdf5/cvefiles/cve-2018-14031.h5",
"cve_hdf5/cvefiles/cve-2018-14033.h5",
"cve_hdf5/cvefiles/cve-2018-14034.h5",
"cve_hdf5/cvefiles/cve-2018-14035.h5",
"cve_hdf5/cvefiles/cve-2018-14460.h5",
"cve_hdf5/cvefiles/cve-2018-15671.h5",
"cve_hdf5/cvefiles/cve-2018-15672.h5",
"cve_hdf5/cvefiles/cve-2018-16438.h5",
"cve_hdf5/cvefiles/cve-2018-17233.h5",
"cve_hdf5/cvefiles/cve-2018-17234.h5",
"cve_hdf5/cvefiles/cve-2018-17237.h5",
"cve_hdf5/cvefiles/cve-2018-17432.h5",
"cve_hdf5/cvefiles/cve-2018-17434.h5",
"cve_hdf5/cvefiles/cve-2018-17435.h5",
"cve_hdf5/cvefiles/cve-2018-17437.h5",
"cve_hdf5/cvefiles/cve-2019-8396.h5",
"cve_hdf5/cvefiles/cve-2019-9152.h5",
"cve_hdf5/cvefiles/cve-2020-10811.h5",
"cve_hdf5/cvefiles/cve-2020-18232.h5",
"cve_hdf5/cvefiles/cve-2021-36977.h5",
"cve_hdf5/cvefiles/cve-2021-37501.h5",
"cve_hdf5/cvefiles/cve-2021-45829.h5",
"cve_hdf5/cvefiles/cve-2021-45833.h5",
"cve_hdf5/cvefiles/cve-2024-29157.h5",
"cve_hdf5/cvefiles/cve-2024-29158.h5",
"cve_hdf5/cvefiles/cve-2024-29159.h5",
"cve_hdf5/cvefiles/cve-2024-29160.h5",
"cve_hdf5/cvefiles/cve-2024-29161.h5",
"cve_hdf5/cvefiles/cve-2024-29162.h5",
"cve_hdf5/cvefiles/cve-2024-29163.h5",
"cve_hdf5/cvefiles/cve-2024-29164.h5",
"cve_hdf5/cvefiles/cve-2024-29165.h5",
"cve_hdf5/cvefiles/cve-2024-29166.h5",
"cve_hdf5/cvefiles/cve-2024-32605.h5",
"cve_hdf5/cvefiles/cve-2024-32606.h5",
"cve_hdf5/cvefiles/cve-2024-32607-1.h5",
"cve_hdf5/cvefiles/cve-2024-32607-2.h5",
"cve_hdf5/cvefiles/cve-2024-32608.h5",
"cve_hdf5/cvefiles/cve-2024-32610.h5",
"cve_hdf5/cvefiles/cve-2024-32611.h5",
"cve_hdf5/cvefiles/cve-2024-32612.h5",
"cve_hdf5/cvefiles/cve-2024-32613.h5",
"cve_hdf5/cvefiles/cve-2024-32614.h5",
"cve_hdf5/cvefiles/cve-2024-32615.h5",
"cve_hdf5/cvefiles/cve-2024-32616.h5",
"cve_hdf5/cvefiles/cve-2024-32617.h5",
"cve_hdf5/cvefiles/cve-2024-32619.h5",
"cve_hdf5/cvefiles/cve-2024-32620.h5",
"cve_hdf5/cvefiles/cve-2024-32621.h5",
"cve_hdf5/cvefiles/cve-2024-32622.h5",
"cve_hdf5/cvefiles/cve-2024-32624.h5",
"cve_hdf5/cvefiles/cve-2024-33873.h5",
"cve_hdf5/cvefiles/cve-2024-33875.h5",
"cve_hdf5/cvefiles/cve-2024-33876.h5",
"cve_hdf5/cvefiles/cve-2024-33877.h5",
"cve_hdf5/cvefiles/cve-2025-2310.h5",
"cve_hdf5/cvefiles/cve-2025-2924.h5",
"cve_hdf5/cvefiles/cve-2025-2925.h5",
"cve_hdf5/cvefiles/cve-2025-6269-1.h5",
"cve_hdf5/cvefiles/cve-2025-6269-2.h5",
"cve_hdf5/cvefiles/cve-2025-6269-3.h5",
"cve_hdf5/cvefiles/cve-2025-6269-4.h5",
"cve_hdf5/cvefiles/cve-2025-6516.h5",
"cve_hdf5/cvefiles/cve-2025-6857.h5",
"cve_hdf5/cvefiles/cve-2025-7067.h5",
"cve_hdf5/cvefiles/cve-2026-26200.h5",
"cve_hdf5/cvefiles/cve-2026-34734.h5",
"cve_hdf5/cvefiles/cve-2026-92627.h5",
"cve_hdf5/cvefiles/unknown-1.h5",
"cve_hdf5/fuzzerfiles/gh-4431-poc-03.h5",
"cve_hdf5/fuzzerfiles/gh-4432-poc-05.h5",
"cve_hdf5/fuzzerfiles/gh-4433-poc-08.h5",
"cve_hdf5/fuzzerfiles/gh-4435-poc-10.h5",
"cve_hdf5/fuzzerfiles/gh_2649_flawed.h5",
"cve_hdf5/fuzzerfiles/gh_2649_plain_model.h5",
"h5py_data/compound-dtype-complex.h5",
"h5py_data/vlen_string_dset.h5",
"h5py_data/vlen_string_dset_utc.h5",
"h5py_data/vlen_string_s390x.h5",
"hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_bitgroom.h5",
"hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_granularbr.h5",
"hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_jpeg.h5",
"hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_lz4.h5",
"hdf5/HDF5Examples/C/H5FLT/tfiles/h5ex_d_zstd.h5",
"hdf5/HDF5Examples/C/H5G/16/h5ex_g_iterate.h5",
"hdf5/HDF5Examples/C/H5G/16/h5ex_g_traverse.h5",
"hdf5/HDF5Examples/C/H5G/h5ex_g_iterate.h5",
"hdf5/HDF5Examples/C/H5G/h5ex_g_traverse.h5",
"hdf5/HDF5Examples/C/H5G/h5ex_g_visit.h5",
"hdf5/HDF5Examples/FORTRAN/H5G/h5ex_g_iterate.h5",
"hdf5/HDF5Examples/FORTRAN/H5G/h5ex_g_traverse.h5",
"hdf5/HDF5Examples/FORTRAN/H5G/h5ex_g_visit.h5",
"hdf5/HDF5Examples/JAVA/H5G/h5ex_g_iterate.h5",
"hdf5/HDF5Examples/JAVA/H5G/h5ex_g_visit.h5",
"hdf5/HDF5Examples/JAVA/compat/H5G/110/h5ex_g_iterate.h5",
"hdf5/HDF5Examples/JAVA/compat/H5G/110/h5ex_g_visit.h5",
"hdf5/HDF5Examples/JAVA/compat/H5G/h5ex_g_iterate.h5",
"hdf5/HDF5Examples/JAVA/compat/H5G/h5ex_g_visit.h5",
"hdf5/c++/test/th5s.h5",
"hdf5/hl/test/testfiles/test_ds_be.h5",
"hdf5/hl/test/testfiles/test_ds_be_new_ref-32bit.h5",
"hdf5/hl/test/testfiles/test_ds_be_new_ref.h5",
"hdf5/hl/test/testfiles/test_ds_le.h5",
"hdf5/hl/test/testfiles/test_ds_le_new_ref.h5",
"hdf5/hl/test/testfiles/test_ld.h5",
"hdf5/hl/test/testfiles/test_table_be.h5",
"hdf5/hl/test/testfiles/test_table_cray.h5",
"hdf5/hl/test/testfiles/test_table_le.h5",
"hdf5/test/testfiles/aggr.h5",
"hdf5/test/testfiles/bad_chunk_ndims.h5",
"hdf5/test/testfiles/bad_compound.h5",
"hdf5/test/testfiles/bad_offset.h5",
"hdf5/test/testfiles/be_data.h5",
"hdf5/test/testfiles/be_extlink1.h5",
"hdf5/test/testfiles/be_extlink2.h5",
"hdf5/test/testfiles/btree_idx_1_6.h5",
"hdf5/test/testfiles/btree_idx_1_8.h5",
"hdf5/test/testfiles/charsets.h5",
"hdf5/test/testfiles/corrupt_stab_msg.h5",
"hdf5/test/testfiles/deflate.h5",
"hdf5/test/testfiles/file_image_core_test.h5",
"hdf5/test/testfiles/filespace_1_6.h5",
"hdf5/test/testfiles/filespace_1_8.h5",
"hdf5/test/testfiles/fill18.h5",
"hdf5/test/testfiles/fill_old.h5",
"hdf5/test/testfiles/filter_error.h5",
"hdf5/test/testfiles/fsm_aggr_nopersist.h5",
"hdf5/test/testfiles/fsm_aggr_persist.h5",
"hdf5/test/testfiles/group_old.h5",
"hdf5/test/testfiles/h5fc_ext1_f.h5",
"hdf5/test/testfiles/h5fc_ext1_i.h5",
"hdf5/test/testfiles/h5fc_ext2_if.h5",
"hdf5/test/testfiles/h5fc_ext2_sf.h5",
"hdf5/test/testfiles/h5fc_ext3_isf.h5",
"hdf5/test/testfiles/h5fc_ext_none.h5",
"hdf5/test/testfiles/le_data.h5",
"hdf5/test/testfiles/le_extlink1.h5",
"hdf5/test/testfiles/le_extlink2.h5",
"hdf5/test/testfiles/memleak_H5O_dtype_decode_helper_H5Odtype.h5",
"hdf5/test/testfiles/mergemsg.h5",
"hdf5/test/testfiles/noencoder.h5",
"hdf5/test/testfiles/none.h5",
"hdf5/test/testfiles/paged_nopersist.h5",
"hdf5/test/testfiles/paged_persist.h5",
"hdf5/test/testfiles/specmetaread.h5",
"hdf5/test/testfiles/tarrold.h5",
"hdf5/test/testfiles/tbad_msg_count.h5",
"hdf5/test/testfiles/tbogus.h5",
"hdf5/test/testfiles/test_filters_be.h5",
"hdf5/test/testfiles/test_filters_le.h5",
"hdf5/test/testfiles/th5s.h5",
"hdf5/test/testfiles/tlayouto.h5",
"hdf5/test/testfiles/tmisc38a.h5",
"hdf5/test/testfiles/tmisc38b.h5",
"hdf5/test/testfiles/tmtimen.h5",
"hdf5/test/testfiles/tmtimeo.h5",
"hdf5/test/testfiles/tnullspace.h5",
"hdf5/test/testfiles/tsizeslheap.h5",
"hdf5/tools/test/testfiles/bigendian/tdset2.h5",
"hdf5/tools/test/testfiles/binfp64.h5",
"hdf5/tools/test/testfiles/binin16.h5",
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"netcdf4-python/examples/data/prmsl.2011.nc",
"netcdf4-python/examples/data/rtofs_glo_3dz_f006_6hrly_reg3.nc",
"netcdf4-python/test/20171025_2056.Cloud_Top_Height.nc",
"netcdf4-python/test/issue1152.nc",
"netcdf4-python/test/issue671.nc",
"netcdf4-python/test/issue672.nc",
"netcdf4-python/test/test_gold.nc",
"usnistgov_h5wasm/test/array.h5",
"usnistgov_h5wasm/test/compressed.h5",
"usnistgov_h5wasm/test/empty.h5",
"usnistgov_h5wasm/test/float16.h5",
"usnistgov_h5wasm/test/vlen.h5",
"xarray-data/ROMS_example.nc",
"xarray-data/basin_mask.nc",
"xarray-data/imerghh_730.hdf5",
"xarray-data/precipitation.nc4"
]
}
conformance/check.py
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@@ -1,88 +0,0 @@
#!/usr/bin/env python3
"""check.py <results_dir> <baseline.json> [--update]
The conformance gate. Fails (exit 1) when
* clawhdf5 panicked, hung, crashed or ran out of memory on any file, or
* the ok count fell below the baseline's, or
* a file the baseline lists as ok is no longer ok (even if another file
became ok and the total held).
New ok files are reported so the baseline can be raised (--update rewrites it
from the results).
"""
import json
import os
import sys
FATAL = ("panic", "hang", "crash", "oom")
def main():
args = [a for a in sys.argv[1:] if not a.startswith("--")]
update = "--update" in sys.argv
res_dir, base_path = args
res = json.load(open(os.path.join(res_dir, "results.json")))
rows = res["rows"]
counts = {}
per_corpus = {}
for r in rows:
counts[r["class"]] = counts.get(r["class"], 0) + 1
pc = per_corpus.setdefault(r["corpus"], {})
pc[r["class"]] = pc.get(r["class"], 0) + 1
ok_files = sorted(r["file"] for r in rows if r["class"] == "ok")
if update:
meta = {}
mp = os.path.join(res_dir, "report-meta.json")
if os.path.exists(mp):
meta = json.load(open(mp))
base = {
"comment": "conformance/run.sh fails if the ok count drops below `ok` or a file in `ok_files` stops being ok. "
"Regenerate with `conformance/run.sh --update-baseline` after an intended change.",
"commit": meta.get("commit", ""),
"date": meta.get("date", ""),
"reference": meta.get("reference", ""),
"files": len(rows),
"ok": len(ok_files),
"counts": dict(sorted(counts.items())),
"per_corpus": {k: dict(sorted(v.items())) for k, v in sorted(per_corpus.items())},
"ok_files": ok_files,
}
with open(base_path, "w") as fh:
json.dump(base, fh, indent=1)
fh.write("\n")
print(f"baseline updated: {len(ok_files)} ok of {len(rows)} files -> {base_path}")
return 0
base = json.load(open(base_path))
failures = []
fatal = [r for r in rows if r["class"] in FATAL]
for r in fatal:
failures.append(f"{r['class']}: {r['file']}: {r['ours_detail'][:200]}")
if len(ok_files) < base["ok"]:
failures.append(f"ok count dropped: {len(ok_files)} < baseline {base['ok']}")
now_ok = set(ok_files)
by_file = {r["file"]: r for r in rows}
for f in base["ok_files"]:
if f not in now_ok:
r = by_file.get(f)
why = f"now {r['class']}: {(r['ours_detail'] or r['first_issue'])[:200]}" if r else "no longer in the corpus"
failures.append(f"regressed: {f}: {why}")
gained = sorted(now_ok - set(base["ok_files"]))
print(f"conformance: {len(ok_files)} ok of {len(rows)} files (baseline {base['ok']} of {base['files']}); "
+ ", ".join(f"{k} {v}" for k, v in sorted(counts.items())))
if gained:
print(f"{len(gained)} file(s) newly ok — raise the baseline with `conformance/run.sh --update-baseline`:")
for f in gained:
print(f" + {f}")
if failures:
print(f"CONFORMANCE GATE FAILED ({len(failures)}):")
for f in failures:
print(f" - {f}")
return 1
print("conformance gate passed")
return 0
if __name__ == "__main__":
sys.exit(main())
conformance/compare.py
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@@ -1,289 +0,0 @@
#!/usr/bin/env python3
"""compare.py <results_dir>: classify each file and group failures by root cause.
Writes <results_dir>/results.csv, results.json and summary.md.
File classes (first match wins):
hang, oom, crash, panic ours: timeout / allocation failure / signal / any panic (caught or not)
h5py-cannot-read libhdf5/h5py failed to open the file (or crashed/hung)
our-error we fail to open, list, or read something h5py reads
mismatch we read something with different shape/values, or a different object set
ok
"""
import collections
import csv
import json
import os
import re
import sys
R = sys.argv[1]
RUNS = os.path.join(R, "runs")
def load(d, name):
rc_p = os.path.join(d, name + ".rc")
if not os.path.exists(rc_p):
return None
rc = int(open(rc_p).read().strip() or -1)
err = open(os.path.join(d, name + ".err"), errors="replace").read()
js = None
try:
js = json.load(open(os.path.join(d, name + ".json")))
except Exception: # noqa: BLE001
pass
return {"rc": rc, "err": err, "json": js}
def proc_status(p):
"""-> (status, detail)"""
if p is None:
return "missing", ""
rc, err = p["rc"], p["err"]
first_panic = next((ln for ln in err.splitlines() if ln.startswith("PANIC:") or "panicked at" in ln), "")
if rc == 0 and p["json"] is not None:
return "ok", ""
if rc == 137 or rc == 124:
return "hang", f"timeout ({os.environ.get('TMO', '20')} s)"
if "memory allocation of" in err or "MemoryError" in err or "std::bad_alloc" in err:
m = re.search(r"memory allocation of \d+ bytes failed", err)
return "oom", m.group(0) if m else "allocation failure"
if "overflowed its stack" in err:
return "crash", "stack overflow"
if rc == 101:
return "panic", first_panic or (err.strip().splitlines() or [""])[-1]
if rc in (134, 139, 136, 135, 132) or rc > 128:
sig = {134: "SIGABRT", 139: "SIGSEGV", 136: "SIGFPE", 135: "SIGBUS", 132: "SIGILL"}.get(rc, f"signal {rc - 128}")
tail = [ln for ln in err.strip().splitlines() if ln.strip()][-1:]
return "crash", f"{sig}: {tail[0][:200] if tail else ''}"
tail = [ln for ln in err.strip().splitlines() if ln.strip()][-1:]
return "crash", f"rc={rc}: {tail[0][:200] if tail else ''}"
def norm(msg):
m = msg.split("\n")[0]
m = re.sub(r"0x[0-9a-fA-F]+", "X", m)
m = re.sub(r'"[^"]*"', '"…"', m)
m = re.sub(r"'[^']*'", "'…'", m)
m = re.sub(r"\d+", "N", m)
return m[:160]
def panic_head(msg):
"""First line + first clawhdf5 frame of a PANIC record."""
lines = msg.split("\n")
frame = next((ln.strip() for ln in lines[1:] if "clawhdf5_format" in ln), "")
return lines[0][:300], frame[:300]
def eq_shape(a, b):
return a == b
rows = []
issues_by_file = {}
root_causes = collections.defaultdict(lambda: {"files": set(), "count": 0, "examples": []})
mismatch_causes = collections.defaultdict(lambda: {"files": set(), "count": 0, "examples": []})
panics = []
ref_only_errors = collections.Counter()
incomparable = collections.Counter()
def add(bucket, key, file, example):
b = bucket[key]
b["count"] += 1
if file not in b["files"] and len(b["examples"]) < 6:
b["examples"].append(example)
b["files"].add(file)
files = [ln.strip() for ln in open(os.path.join(R, "files.txt")) if ln.strip()]
for rel in files:
d = os.path.join(RUNS, rel.replace("/", "__"))
corpus = rel.split("/")[0]
ours, ref = load(d, "ours"), load(d, "ref")
h5dump = load(d, "h5dump")
os_, od = proc_status(ours)
rs, rd = proc_status(ref)
oj = ours["json"] if ours else None
rj = ref["json"] if ref else None
issues = [] # (kind, detail)
caught_panics = []
def scan_err(path, what, msg):
if msg.startswith("PANIC:"):
caught_panics.append((path, what, msg))
if oj:
for o in oj.get("objects", []):
for k in ("error", "attrs_error", "list_error"):
if k in o:
scan_err(o["path"], k, o[k])
for an, av in (o.get("attrs") or {}).items():
if "error" in av:
scan_err(o["path"], f"attr {an}", av["error"])
if oj.get("open_error", "").startswith("PANIC:"):
caught_panics.append(("<open>", "open", oj["open_error"]))
ref_open_fail = rs != "ok" or (rj is not None and "open_error" in rj)
ours_open_err = oj.get("open_error") if oj else None
n_obj = n_ok = 0
if os_ == "ok" and rj and not ref_open_fail and not ours_open_err:
ro = {x["path"]: x for x in rj.get("objects", [])}
oo = {x["path"]: x for x in oj.get("objects", [])}
our_list_errors = [x for x in oo.values() if "list_error" in x]
for p in sorted(set(ro) | set(oo)):
a, b = ro.get(p), oo.get(p)
n_obj += 1
if a is None:
issues.append(("mismatch", f"extra object {p} (kind={b.get('kind')})", "extra-object", b))
continue
if b is None:
if our_list_errors:
continue # accounted for by the list_error
issues.append(("mismatch", f"missing object {p} (kind={a.get('kind')})", "missing-object", a))
continue
ok = True
if a.get("kind") != b.get("kind") and "error" not in b and "error" not in a:
issues.append(("mismatch", f"{p}: kind {a.get('kind')} vs ours {b.get('kind')}", "kind", b))
ok = False
for k in ("error", "list_error", "attrs_error"):
if k in b and k not in a:
issues.append(("our-error", f"{p}: {k}: {b[k]}", b[k], b))
ok = False
elif k in a and k not in b and k == "error":
ref_only_errors[norm(a[k])] += 1
if a.get("kind") == "dataset" and "error" not in a and "error" not in b:
if "skipped" in a or "skipped" in b:
pass
elif a.get("converted"):
incomparable[f"dataset {a['converted']}"] += 1
elif a.get("shape") != b.get("shape"):
issues.append(("mismatch", f"{p}: shape {a.get('shape')} vs ours {b.get('shape')}", "shape", b))
ok = False
elif a.get("hash") != b.get("hash"):
issues.append(("mismatch", f"{p}: values differ (h5py {a.get('dtype')} vs ours {b.get('dtype')})", "values", b | {"ref_head": a.get("head"), "ref_dtype": a.get("dtype")}))
ok = False
ra, oa = a.get("attrs") or {}, b.get("attrs") or {}
if "attrs_error" not in b and "attrs_error" not in a:
for an in sorted(set(ra) | set(oa)):
x, y = ra.get(an), oa.get(an)
if x is None:
issues.append(("mismatch", f"{p}@{an}: extra attribute", "extra-attr", y or {}))
elif y is None:
issues.append(("mismatch", f"{p}@{an}: missing attribute", "missing-attr", x))
elif "error" in y and "error" not in x:
issues.append(("our-error", f"{p}@{an}: {y['error']}", y["error"], y))
elif "error" in x:
continue
elif x.get("converted"):
incomparable[f"attr {x['converted']}"] += 1
elif x.get("shape") != y.get("shape"):
issues.append(("mismatch", f"{p}@{an}: attr shape {x.get('shape')} vs ours {y.get('shape')}", "attr-shape", y | {"ref_dtype": x.get("dtype")}))
elif x.get("hash") != y.get("hash"):
issues.append(("mismatch", f"{p}@{an}: attr values differ (h5py {x.get('dtype')} vs ours {y.get('dtype')})", "attr-values", y | {"ref_head": x.get("head"), "ref_dtype": x.get("dtype")}))
if ok:
n_ok += 1
# classify
if os_ in ("hang", "oom", "crash", "panic"):
cls = os_
elif caught_panics:
cls = "panic"
elif ref_open_fail:
cls = "h5py-cannot-read"
elif ours_open_err:
cls = "our-error"
issues.append(("our-error", f"open: {ours_open_err}", ours_open_err, {}))
elif any(i[0] == "our-error" for i in issues):
cls = "our-error"
elif issues:
cls = "mismatch"
else:
cls = "ok"
if os_ in ("hang", "oom", "crash", "panic") or caught_panics:
panics.append({
"file": rel, "class": cls, "detail": od,
"stderr": (ours["err"] if ours else "")[:3000],
"caught": [(p, w, m[:2500]) for p, w, m in caught_panics[:3]],
"n_caught": len(caught_panics),
})
for kind, detail, key, rec in issues:
if kind == "our-error":
add(root_causes, norm(key), rel, detail[:300])
else:
if key in ("values", "attr-values", "shape", "attr-shape"):
mk = f"{key}: ours={rec.get('dtype')} h5py={rec.get('ref_dtype')} layout={rec.get('layout','-')} filters={rec.get('filters','-')}"
else:
mk = key
add(mismatch_causes, mk, rel, detail[:300] + (f" | ref_head={rec.get('ref_head')} our_head={rec.get('head')}" if rec.get("ref_head") else ""))
ref_detail = rd if rs != "ok" else ((rj or {}).get("open_error") or "")
h5d = ""
if h5dump:
rc = h5dump["rc"]
h5d = {0: "ok", 1: "error", 137: "hang", 124: "hang", 134: "SIGABRT", 139: "SIGSEGV", 136: "SIGFPE", 135: "SIGBUS"}.get(rc, f"rc={rc}")
if "memory allocation" in h5dump["err"] or "Cannot allocate" in h5dump["err"]:
h5d += "(oom)"
rows.append({
"file": rel, "corpus": corpus, "class": cls,
"ours": os_ if os_ != "ok" else ("open-error" if ours_open_err else ("panic" if caught_panics else "ok")),
"ours_detail": (od or ours_open_err or (caught_panics[0][2].split("\n")[0] if caught_panics else ""))[:300],
"ref": rs if rs != "ok" else ("open-error" if (rj or {}).get("open_error") else "ok"),
"ref_detail": ref_detail[:300],
"h5dump_1_14_6": h5d,
"h5dump_detail": ([ln for ln in h5dump["err"].splitlines() if ln.strip()][-1:] or [""])[0][:200] if h5dump else "",
"objects": n_obj, "objects_ok": n_ok,
"issues": len(issues), "first_issue": issues[0][1][:300] if issues else "",
"superblock": (oj or {}).get("superblock_version", ""),
})
# the first issues of each file, for report.py's known-cause matching
issues_by_file[rel] = [
{"kind": k, "key": key, "detail": det[:300], "ours_dtype": rec.get("dtype"), "ref_dtype": rec.get("ref_dtype")}
for k, det, key, rec in issues[:50]
]
with open(os.path.join(R, "results.csv"), "w", newline="") as fh:
w = csv.DictWriter(fh, fieldnames=list(rows[0].keys()))
w.writeheader()
w.writerows(rows)
def ser(b):
return {k: {"files": len(v["files"]), "count": v["count"], "examples": v["examples"], "file_list": sorted(v["files"])} for k, v in sorted(b.items(), key=lambda kv: -len(kv[1]["files"]))}
json.dump({"rows": rows, "issues": issues_by_file, "root_causes": ser(root_causes), "mismatch_causes": ser(mismatch_causes),
"panics": panics, "incomparable": incomparable.most_common(), "ref_only_errors": ref_only_errors.most_common()},
open(os.path.join(R, "results.json"), "w"), indent=1)
classes = ["ok", "our-error", "mismatch", "h5py-cannot-read", "hang", "panic", "crash", "oom"]
by_corpus = collections.defaultdict(collections.Counter)
for r in rows:
by_corpus[r["corpus"]][r["class"]] += 1
by_corpus["ALL"][r["class"]] += 1
lines = ["# Conformance sweep summary", "", "| corpus | files | " + " | ".join(classes) + " |", "|---" * (len(classes) + 2) + "|"]
for c in sorted(by_corpus, key=lambda k: (k == "ALL", k)):
cnt = by_corpus[c]
lines.append(f"| {c} | {sum(cnt.values())} | " + " | ".join(str(cnt.get(k, 0)) for k in classes) + " |")
lines += ["", "## Panics / hangs / crashes / OOM", ""]
for p in panics:
lines.append(f"- **{p['file']}** [{p['class']}] {p['detail']}")
for path, what, m in p["caught"][:1]:
lines.append(" ```\n " + f"{path} ({what}): " + m.replace("\n", "\n ")[:1500] + "\n ```")
if not p["caught"] and p["stderr"]:
lines.append(" ```\n " + p["stderr"].strip()[:1500].replace("\n", "\n ") + "\n ```")
lines += ["", "## Our-error root causes (files affected)", ""]
for k, v in ser(root_causes).items():
lines.append(f"- [{v['files']} files, {v['count']} objs] `{k}`")
for ex in v["examples"][:3]:
lines.append(f" - {ex}")
lines += ["", "## Mismatch root causes", ""]
for k, v in ser(mismatch_causes).items():
lines.append(f"- [{v['files']} files, {v['count']} objs] `{k}`")
for ex in v["examples"][:3]:
lines.append(f" - {ex}")
lines += ["", "## Objects h5py fails on but we read (top)", ""]
for k, n in ref_only_errors.most_common(15):
lines.append(f"- {n} x `{k}`")
open(os.path.join(R, "summary.md"), "w").write("\n".join(lines) + "\n")
print("\n".join(lines[:4 + len(by_corpus)]))
conformance/corpus.txt
-19
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@@ -1,19 +0,0 @@
# Conformance corpora, pinned by commit. fetch-corpus.sh reads this file.
#
# name git-url commit root [sparse-checkout patterns...]
#
# `root` is the directory inside the checkout that is swept ("." = all of it).
# Patterns are git non-cone sparse-checkout patterns; none = whole repository.
# Every file under <root> with an HDF5/netCDF-4 extension is probed; for
# cve_hdf5 the extension-less files in cvefiles/ and fuzzerfiles/ are too.
# Licences: each corpus keeps its upstream licence; nothing here is committed
# to this repository — the files are downloaded into the gitignored cache.
hdf5 https://github.com/HDFGroup/hdf5.git a3cf1ea82cc7a66e50029a688121e1b105a7ce88 . *.h5 *.he5 *.nc *.hdf5 *.h5f
cve_hdf5 https://github.com/HDFGroup/cve_hdf5.git 3fd1f5ae3869e01b8ae02b41d7108de7ffb1a374 .
netcdf-c https://github.com/Unidata/netcdf-c.git beb7b9585273c1548386231a59b809d906359033 . /nc_test4/*.nc /ncdump/*.nc /nc_test4/*.h5 /ncdump/*.h5 /h5_test/*.h5 /hdf5_test/*.h5
NCAS-CMS_pyfive https://github.com/NCAS-CMS/pyfive.git 8cf07b8749133f41c5e30b8a4c604486f687fe74 . *.h5 *.hdf5 *.hdf *.nc *.he5
usnistgov_h5wasm https://github.com/usnistgov/h5wasm.git 02f6336527d2812783fcedabfbf42127ec8d06d2 . *.h5 *.hdf5 *.hdf *.nc *.he5
netcdf4-python https://github.com/Unidata/netcdf4-python.git 6e67576d39aef8091fb20bd767b4f1a52ddc1bec . *.nc *.h5
xarray-data https://github.com/pydata/xarray-data.git a35297e9da2cc99c811014f0c8a4297345a5c28d . /basin_mask.nc /precipitation.nc4 /imerghh_730.hdf5 /eraint_uvz.nc /ROMS_example.nc /tiny.nc
# h5py 3.16.0 (tag 3.16.0), its test data files.
h5py_data https://github.com/h5py/h5py.git b2f0347c4200333acd89b43733f1caa0c115162f h5py/tests/data_files /h5py/tests/data_files/*
conformance/fetch-corpus.sh
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@@ -1,39 +0,0 @@
#!/usr/bin/env bash
# fetch-corpus.sh [cache_dir]
#
# Download the corpora pinned in conformance/corpus.txt into the (gitignored)
# cache: <cache>/src/<name> is a shallow, sparse, blob-filtered checkout of the
# pinned commit and <cache>/corpus/<name> links to the swept root inside it.
# A corpus already checked out at its pinned commit is left alone, so a second
# run costs nothing and needs no network.
set -euo pipefail
HERE="$(cd "$(dirname "$0")" && pwd)"
CACHE="${1:-${CONFORMANCE_CACHE:-$HERE/.cache}}"
mkdir -p "$CACHE/src" "$CACHE/corpus"
CACHE="$(cd "$CACHE" && pwd)"
retry() { local i; for i in 1 2 3 4; do "$@" && return 0; sleep $((i * 5)); done; return 1; }
grep -v '^[[:space:]]*\(#\|$\)' "$HERE/corpus.txt" | while read -r name url commit root patterns; do
src="$CACHE/src/$name"
if [ -d "$src/.git" ] && [ "$(git -C "$src" rev-parse HEAD 2>/dev/null)" = "$commit" ]; then
echo "cached $name @ ${commit:0:12}"
else
echo "fetching $name @ ${commit:0:12} from $url"
rm -rf "$src"
git init -q "$src"
git -C "$src" remote add origin "$url"
git -C "$src" config advice.detachedHead false
if [ -n "$patterns" ]; then
git -C "$src" config core.sparseCheckout true
# no-cone patterns (globs); `set -f` keeps the shell from expanding them
(set -f; printf '%s\n' $patterns) > "$src/.git/info/sparse-checkout"
fi
retry git -C "$src" fetch -q --depth 1 --filter=blob:none origin "$commit"
retry git -C "$src" checkout -q FETCH_HEAD
got="$(git -C "$src" rev-parse HEAD)"
[ "$got" = "$commit" ] || { echo "error: $name checked out $got, expected $commit" >&2; exit 1; }
fi
ln -sfn "$src/$root" "$CACHE/corpus/$name"
done
echo "corpus ready in $CACHE/corpus"
conformance/list_files.py
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@@ -1,48 +0,0 @@
#!/usr/bin/env python3
"""list_files.py <corpus_dir>: print the files the sweep probes, one per line,
as <corpus>/<path> in byte order.
* every file named *.h5 *.hdf5 *.he5 *.nc *.nc4 *.hdf *.h5f in each corpus,
except netCDF classic / 64-bit-offset / CDF5 files (magic "CDF"): they are
not HDF5, so neither side can read them and they say nothing;
* plus, for cve_hdf5, every file in cvefiles/ and fuzzerfiles/ except
.md/.c sources — the reproducers are mostly extension-less, and they are
kept whatever their bytes look like (that is their point).
"""
import os
import sys
EXTS = (".h5", ".hdf5", ".he5", ".nc", ".nc4", ".hdf", ".h5f")
def walk(top):
for dirpath, dirnames, filenames in os.walk(top):
dirnames[:] = [d for d in dirnames if d != ".git"]
for fn in filenames:
p = os.path.join(dirpath, fn)
if os.path.isfile(p) and not os.path.islink(p):
yield os.path.relpath(p, top)
def main(root):
out = set()
for corpus in sorted(os.listdir(root)):
top = os.path.join(root, corpus)
if not os.path.isdir(top):
continue
for rel in walk(top):
path = os.path.join(top, rel)
if rel.lower().endswith(EXTS):
with open(path, "rb") as fh:
if fh.read(3) == b"CDF":
continue
out.add(f"{corpus}/{rel}")
elif corpus == "cve_hdf5" and rel.split(os.sep)[0] in ("cvefiles", "fuzzerfiles") \
and not rel.endswith((".md", ".c")):
out.add(f"{corpus}/{rel}")
for f in sorted(out, key=lambda s: s.encode()):
print(f)
if __name__ == "__main__":
main(sys.argv[1])
conformance/probe/Cargo.lock
Generated
-458
View File
@@ -1,458 +0,0 @@
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conformance/probe/Cargo.toml
-25
View File
@@ -1,25 +0,0 @@
[package]
name = "conformance-probe"
version = "0.1.0"
edition = "2024"
rust-version = "1.92"
publish = false
description = "Walks an HDF5 file with clawhdf5-format and prints a canonical JSON description (see conformance/README.md)"
# Deliberately outside the main workspace: `cargo test --workspace` never
# builds it, and it links the optional C codecs (zstd, libaec) that the core
# crates' default build must not.
[workspace]
[dependencies]
clawhdf5-format = { path = "../../crates/clawhdf5-format", features = ["lz4", "zstd", "szip", "pcodec"] }
serde_json = "1"
sha2 = "0.10"
[profile.release]
# Keep panics catchable (the probe records them per object) and turn integer
# overflow into a reported panic instead of silent wraparound.
debug = 1
overflow-checks = true
debug-assertions = true
panic = "unwind"
conformance/probe/src/main.rs
-898
View File
@@ -1,898 +0,0 @@
//! Conformance probe: walks an HDF5 file with clawhdf5-format (the same calls
//! the `clawhdf5` facade makes) and prints a canonical JSON description:
//! every hard-linked object (sorted-name DFS, deduplicated by header address),
//! and for each dataset / attribute its shape plus the SHA-256 of its values
//! in a canonical encoding shared with `ref.py`.
//!
//! Canonical value encoding (per element, concatenated, row-major):
//! int / float / bitfield / enum / time : element bytes, little-endian
//! non-IEEE-layout float (e.g. N-Bit) : the IEEE float of the same size it converts to
//! int with bit offset / short precision: the full-width integer it converts to
//! opaque : raw bytes
//! compound : members in declaration order (padding dropped)
//! array : base elements row-major
//! string (fixed or VL) : b'S' + u32le len + bytes (cut at first NUL, trailing spaces stripped)
//! VL sequence : b'V' + u32le count + base elements
//! reference : b'R' (payload not compared)
//!
//! Every object is processed inside catch_unwind; a caught panic is recorded
//! with its message, location and the clawhdf5 frames of its backtrace.
use std::cell::RefCell;
use std::collections::{HashMap, HashSet};
use std::panic::{self, AssertUnwindSafe};
use std::rc::Rc;
use clawhdf5_format::attribute::extract_attributes_full;
use clawhdf5_format::data_layout::DataLayout;
use clawhdf5_format::data_read;
use clawhdf5_format::dataspace::{Dataspace, DataspaceType};
use clawhdf5_format::datatype::{Datatype, DatatypeByteOrder};
use clawhdf5_format::filter_pipeline::FilterPipeline;
use clawhdf5_format::global_heap::GlobalHeapCollection;
use clawhdf5_format::group_v1::{self, GroupEntry};
use clawhdf5_format::group_v2;
use clawhdf5_format::message_type::MessageType;
use clawhdf5_format::object_header::ObjectHeader;
use clawhdf5_format::signature;
use clawhdf5_format::superblock::Superblock;
use clawhdf5_format::symbol_table::SymbolTableMessage;
use serde_json::{Map, Value, json};
use sha2::{Digest, Sha256};
const MAX_BYTES: u64 = 200 * 1024 * 1024;
const MAX_OBJECTS: usize = 200_000;
thread_local! {
static LAST_PANIC: RefCell<Option<String>> = const { RefCell::new(None) };
}
fn install_hook() {
panic::set_hook(Box::new(|info| {
let msg = if let Some(s) = info.payload().downcast_ref::<&str>() {
s.to_string()
} else if let Some(s) = info.payload().downcast_ref::<String>() {
s.clone()
} else {
"<non-string panic>".into()
};
let loc = info
.location()
.map(|l| format!("{}:{}", l.file(), l.line()))
.unwrap_or_default();
let bt = std::backtrace::Backtrace::force_capture().to_string();
// keep only frames from clawhdf5 code
let mut frames = Vec::new();
let lines: Vec<&str> = bt.lines().collect();
for (i, l) in lines.iter().enumerate() {
let t = l.trim();
if t.contains("clawhdf5_format::") || t.contains("conformance_probe::") {
let at = lines
.get(i + 1)
.map(|n| n.trim())
.filter(|n| n.starts_with("at "))
.map(|n| {
let n = n.trim_start_matches("at ");
match n.find("/crates/") {
Some(p) => n[p + 1..].to_string(),
None => n.to_string(),
}
})
.unwrap_or_default();
let name = t.split_once(": ").map(|x| x.1).unwrap_or(t);
frames.push(format!("{name} ({at})"));
if frames.len() >= 12 {
break;
}
}
}
let full = format!("PANIC: {msg} @ {loc}\n {}", frames.join("\n "));
eprintln!("{full}");
LAST_PANIC.with(|p| *p.borrow_mut() = Some(full));
}));
}
/// Run `f`, turning a panic into Err("PANIC: ...").
fn guarded<T>(f: impl FnOnce() -> Result<T, String>) -> Result<T, String> {
match panic::catch_unwind(AssertUnwindSafe(f)) {
Ok(r) => r,
Err(_) => Err(LAST_PANIC
.with(|p| p.borrow_mut().take())
.unwrap_or_else(|| "PANIC: <unknown>".into())),
}
}
fn e<E: std::fmt::Debug>(x: E) -> String {
format!("{x:?}")
}
struct Ctx<'a> {
data: &'a [u8],
os: u8,
ls: u8,
base_dir: std::path::PathBuf,
heaps: RefCell<HashMap<u64, Result<Rc<GlobalHeapCollection>, String>>>,
}
impl<'a> Ctx<'a> {
fn header(&self, addr: u64) -> Result<ObjectHeader, String> {
ObjectHeader::parse(self.data, addr as usize, self.os, self.ls).map_err(e)
}
fn payload(&self, h: &ObjectHeader, t: MessageType) -> Result<Option<Vec<u8>>, String> {
match h.messages.iter().find(|m| m.msg_type == t) {
None => Ok(None),
Some(m) => {
clawhdf5_format::shared_message::message_data(self.data, m, self.os, self.ls)
.map(|c| Some(c.into_owned()))
.map_err(e)
}
}
}
fn heap_obj(&self, addr: u64, idx: u32) -> Result<Vec<u8>, String> {
let coll = {
let mut cache = self.heaps.borrow_mut();
cache
.entry(addr)
.or_insert_with(|| {
GlobalHeapCollection::parse(self.data, addr as usize, self.ls)
.map(Rc::new)
.map_err(e)
})
.clone()?
};
coll.get_object(idx as u16)
.map(|o| o.data.clone())
.ok_or_else(|| {
format!("GlobalHeapObjectNotFound {{ collection_address: {addr}, index: {idx} }}")
})
}
fn read_offset(&self, b: &[u8]) -> u64 {
let mut v = 0u64;
for (i, x) in b.iter().take(self.os as usize).enumerate() {
v |= (*x as u64) << (8 * i);
}
v
}
fn canon(&self, dt: &Datatype, b: &[u8], out: &mut Vec<u8>) -> Result<(), String> {
let size = dt.type_size() as usize;
if b.len() < size {
return Err(format!(
"canon: element slice {} < type size {size}",
b.len()
));
}
match dt {
Datatype::FloatingPoint { .. } if !ieee_layout(dt) => {
canon_custom_float(dt, &b[..size], out)?
}
Datatype::FixedPoint { .. } if partial_int(dt) => {
canon_partial_int(dt, &b[..size], out)?
}
Datatype::FixedPoint { byte_order, .. }
| Datatype::BitField { byte_order, .. }
| Datatype::FloatingPoint { byte_order, .. } => match byte_order {
DatatypeByteOrder::LittleEndian => out.extend_from_slice(&b[..size]),
DatatypeByteOrder::BigEndian => out.extend(b[..size].iter().rev()),
DatatypeByteOrder::Vax => return Err("canon: VAX byte order".into()),
},
Datatype::Time { .. } | Datatype::Opaque { .. } => out.extend_from_slice(&b[..size]),
Datatype::String { .. } => canon_str(&b[..size], out),
Datatype::Compound { members, .. } => {
for m in members {
let off = m.byte_offset as usize;
let ms = m.datatype.type_size() as usize;
if off.checked_add(ms).is_none_or(|end| end > size) {
return Err(format!("canon: member {} out of bounds", m.name));
}
self.canon(&m.datatype, &b[off..off + ms], out)?;
}
}
Datatype::Reference { .. } => out.push(b'R'),
Datatype::Enumeration { base_type, .. } => self.canon(base_type, b, out)?,
Datatype::Array {
base_type,
dimensions,
} => {
let n: usize = dimensions.iter().map(|d| *d as usize).product();
let bs = base_type.type_size() as usize;
for i in 0..n {
self.canon(base_type, &b[i * bs..], out)?;
}
}
Datatype::VariableLength {
is_string,
base_type,
..
} => {
let len = u32::from_le_bytes([b[0], b[1], b[2], b[3]]) as usize;
let addr = self.read_offset(&b[4..]);
let idx_off = 4 + self.os as usize;
let idx = u32::from_le_bytes([
b[idx_off],
b[idx_off + 1],
b[idx_off + 2],
b[idx_off + 3],
]);
let obj = if len == 0 || addr == 0 || addr == u64::MAX >> (64 - 8 * self.os as u32)
{
Vec::new()
} else {
self.heap_obj(addr, idx)?
};
if *is_string {
let l = len.min(obj.len());
canon_str(&obj[..l], out);
} else {
let bs = base_type.type_size() as usize;
if bs == 0 {
return Err("canon: VL base size 0".into());
}
let need = len.checked_mul(bs).ok_or("canon: VL overflow")?;
if len > 0 && obj.len() < need {
return Err(format!("canon: VL object {} < {need}", obj.len()));
}
out.push(b'V');
out.extend_from_slice(&(len as u32).to_le_bytes());
for i in 0..len {
self.canon(base_type, &obj[i * bs..], out)?;
}
}
}
}
Ok(())
}
/// Returns (shape json, n_elements)
fn shape(ds: &Dataspace) -> (Value, u64) {
match ds.space_type {
DataspaceType::Null => (Value::String("null".into()), 0),
DataspaceType::Scalar => (json!([]), 1),
DataspaceType::Simple => {
let n = ds.dimensions.iter().fold(1u64, |a, d| a.saturating_mul(*d));
(json!(ds.dimensions), n)
}
}
}
fn hash_values(
&self,
dt: &Datatype,
raw: &[u8],
n: u64,
rec: &mut Map<String, Value>,
) -> Result<(), String> {
let size = dt.type_size() as usize;
let need = (n as usize).checked_mul(size).ok_or("n*size overflow")?;
if raw.len() != need {
return Err(format!(
"raw length {} != n_elements {n} * type_size {size}",
raw.len()
));
}
let mut canon = Vec::with_capacity(need);
for i in 0..n as usize {
self.canon(dt, &raw[i * size..(i + 1) * size], &mut canon)?;
}
let h = Sha256::digest(&canon);
rec.insert("hash".into(), Value::String(hex(&h)));
rec.insert(
"head".into(),
Value::String(hex(&canon[..canon.len().min(48)])),
);
Ok(())
}
/// VDS source files resolve next to the virtual file; like the library,
/// refuse absolute paths and `..`.
fn vds_resolver(
&self,
) -> impl Fn(&str) -> Result<Option<Vec<u8>>, clawhdf5_format::error::FormatError> + use<> {
let base = self.base_dir.clone();
move |name: &str| {
use clawhdf5_format::error::FormatError;
let p = std::path::Path::new(name);
if p.is_absolute()
|| p.components()
.any(|c| matches!(c, std::path::Component::ParentDir))
{
return Err(FormatError::ChunkedReadError(format!("refused {name}")));
}
match std::fs::read(base.join(p)) {
Ok(b) => Ok(Some(b)),
Err(err) if err.kind() == std::io::ErrorKind::NotFound => Ok(None),
Err(err) => Err(FormatError::ChunkedReadError(err.to_string())),
}
}
}
fn read_dataset(&self, h: &ObjectHeader, rec: &mut Map<String, Value>) -> Result<(), String> {
let dtb = self
.payload(h, MessageType::Datatype)?
.ok_or("MissingMessage(Datatype)")?;
let (dt, _) = Datatype::parse(&dtb).map_err(e)?;
rec.insert("dtype".into(), Value::String(dtype_str(&dt)));
let dsb = self
.payload(h, MessageType::Dataspace)?
.ok_or("MissingMessage(Dataspace)")?;
let mut ds = Dataspace::parse(&dsb, self.ls).map_err(e)?;
// A virtual dataset's extent can come from its sources (unlimited /
// printf mappings), as h5py reports it, rather than the stored one.
if let Some(lm) = h
.messages
.iter()
.find(|m| m.msg_type == MessageType::DataLayout)
&& let Ok(dl @ DataLayout::Virtual { .. }) =
DataLayout::parse(&lm.data, self.os, self.ls)
{
let resolver = self.vds_resolver();
ds.dimensions = clawhdf5_format::vds::virtual_dataset_extent(
self.data,
&dl,
&ds,
self.os,
self.ls,
Some(&resolver),
)
.map_err(e)?;
}
let (shape, n) = Self::shape(&ds);
rec.insert("shape".into(), shape);
if n.saturating_mul(dt.type_size() as u64) > MAX_BYTES {
rec.insert("skipped".into(), Value::String("too large".into()));
return Ok(());
}
let lm = h
.messages
.iter()
.find(|m| m.msg_type == MessageType::DataLayout)
.ok_or("MissingMessage(DataLayout)")?;
let dl = DataLayout::parse(&lm.data, self.os, self.ls).map_err(e)?;
rec.insert(
"layout".into(),
Value::String(
match &dl {
DataLayout::Compact { .. } => "compact",
DataLayout::Contiguous { .. } => "contiguous",
DataLayout::Chunked { .. } => "chunked",
DataLayout::Virtual { .. } => "virtual",
}
.into(),
),
);
let pipeline = match self.payload(h, MessageType::FilterPipeline)? {
Some(p) => Some(FilterPipeline::parse(&p).map_err(e)?),
None => None,
};
if let Some(p) = &pipeline {
rec.insert(
"filters".into(),
json!(p.filters.iter().map(|f| f.filter_id).collect::<Vec<_>>()),
);
}
let raw = if matches!(dl, DataLayout::Virtual { .. }) {
let resolver = self.vds_resolver();
let fill = clawhdf5_format::fill_value::dataset_fill_value_in(
self.data,
&h.messages,
self.os,
self.ls,
)
.map_err(e)?;
clawhdf5_format::vds::read_virtual_dataset(
self.data,
&dl,
&ds,
&dt,
fill.as_deref(),
self.os,
self.ls,
Some(&resolver),
)
.map_err(e)?
.data
} else {
let cache = clawhdf5_format::chunk_cache::ChunkCache::new();
clawhdf5_format::fill_value::read_full_with_fill::<clawhdf5_format::error::FormatError>(
&h.messages,
self.data,
&dl,
&ds,
dt.type_size() as usize,
self.os,
self.ls,
|| {
data_read::read_raw_data_cached(
self.data,
&dl,
&ds,
&dt,
pipeline.as_ref(),
self.os,
self.ls,
&cache,
)
},
)
.map_err(e)?
};
self.hash_values(&dt, &raw, n, rec)
}
fn attrs(&self, h: &ObjectHeader) -> Result<Map<String, Value>, String> {
let msgs = extract_attributes_full(self.data, h, self.os, self.ls).map_err(e)?;
let mut out = Map::new();
for a in &msgs {
let r = guarded(|| {
let mut rec = Map::new();
rec.insert("dtype".into(), Value::String(dtype_str(&a.datatype)));
let (shape, n) = Self::shape(&a.dataspace);
rec.insert("shape".into(), shape);
self.hash_values(&a.datatype, &a.raw_data, n, &mut rec)?;
Ok(rec)
});
let v = match r {
Ok(rec) => Value::Object(rec),
Err(msg) => json!({ "error": msg }),
};
out.insert(a.name.clone(), v);
}
Ok(out)
}
fn entries(&self, h: &ObjectHeader) -> Result<Vec<GroupEntry>, String> {
let v1 = h
.messages
.iter()
.find(|m| m.msg_type == MessageType::SymbolTable);
if let Some(m) = v1 {
let stm = SymbolTableMessage::parse(&m.data, self.os).map_err(e)?;
group_v1::resolve_v1_group_entries(self.data, &stm, self.os, self.ls).map_err(e)
} else if h
.messages
.iter()
.any(|m| m.msg_type == MessageType::LinkInfo || m.msg_type == MessageType::Link)
{
group_v2::resolve_v2_group_entries(self.data, h, self.os, self.ls).map_err(e)
} else {
Ok(Vec::new())
}
}
}
/// Element bytes as an unsigned integer (at most 16 bytes), honouring byte order.
fn element_bits(b: &[u8], byte_order: &DatatypeByteOrder) -> Result<u128, String> {
if b.len() > 16 {
return Err(format!("canon: {}-byte numeric element", b.len()));
}
let mut v = 0u128;
match byte_order {
DatatypeByteOrder::LittleEndian => {
for (i, x) in b.iter().enumerate() {
v |= u128::from(*x) << (8 * i);
}
}
DatatypeByteOrder::BigEndian => {
for x in b {
v = (v << 8) | u128::from(*x);
}
}
DatatypeByteOrder::Vax => return Err("canon: VAX byte order".into()),
}
Ok(v)
}
fn field(v: u128, pos: u32, len: u32) -> u128 {
if len == 0 || pos >= 128 {
return 0;
}
let v = v >> pos;
if len >= 128 {
v
} else {
v & ((1u128 << len) - 1)
}
}
/// True when a float's bit fields are exactly IEEE 754 binary16/32/64 for its
/// size. h5py hands back such a type's bytes untouched; any other layout (an
/// N-Bit `H5Tset_precision` float, say) is *converted* by libhdf5 into the
/// numpy float of the same size, so comparing raw bytes would be meaningless.
fn ieee_layout(dt: &Datatype) -> bool {
let Datatype::FloatingPoint {
size,
bit_offset,
bit_precision,
exponent_location,
exponent_size,
mantissa_location,
mantissa_size,
exponent_bias,
..
} = dt
else {
return true;
};
let std = match size {
2 => (16, 10, 5, 10, 15),
4 => (32, 23, 8, 23, 127),
8 => (64, 52, 11, 52, 1023),
_ => return true, // no same-size numpy float to convert to: compare raw
};
*bit_offset == 0
&& (
*bit_precision,
*exponent_location,
*exponent_size,
*mantissa_size,
*exponent_bias,
) == (std.0, std.1, std.2, std.3, std.4)
&& *mantissa_location == 0
}
/// Canonicalise a non-IEEE-layout float the way libhdf5's float->float
/// conversion presents it to h5py: as the IEEE float of the same size.
/// Assumes the implied-leading-one normalisation and the sign bit at the top
/// of the precision (what `H5Tset_precision` produces; the parser does not
/// keep either field).
fn canon_custom_float(dt: &Datatype, b: &[u8], out: &mut Vec<u8>) -> Result<(), String> {
let Datatype::FloatingPoint {
size,
byte_order,
bit_offset,
bit_precision,
exponent_location,
exponent_size,
mantissa_location,
mantissa_size,
exponent_bias,
} = dt
else {
unreachable!()
};
let (esize, msize) = (u32::from(*exponent_size), u32::from(*mantissa_size));
if esize == 0 || esize > 30 || msize > 64 {
return Err(format!("canon: unsupported float layout e{esize} m{msize}"));
}
let v = element_bits(b, byte_order)?;
let sign_pos = (u32::from(*bit_offset) + u32::from(*bit_precision)).saturating_sub(1);
let neg = field(v, sign_pos, 1) == 1;
let e = field(v, u32::from(*exponent_location), esize) as i64;
let m = field(v, u32::from(*mantissa_location), msize);
let emax = (1i64 << esize) - 1;
let bias = i64::from(*exponent_bias);
let mag = if e == emax {
if m == 0 { f64::INFINITY } else { f64::NAN }
} else if e == 0 {
(m as f64) * 2f64.powi((1 - bias - msize as i64) as i32)
} else {
((1u128 << msize) as f64 + m as f64) * 2f64.powi((e - bias - msize as i64) as i32)
};
let x = if neg { -mag } else { mag };
match size {
2 => out
.extend_from_slice(&clawhdf5_format::float16::f32_to_f16_bits(x as f32).to_le_bytes()),
4 => out.extend_from_slice(&(x as f32).to_le_bytes()),
8 => out.extend_from_slice(&x.to_le_bytes()),
_ => unreachable!("ieee_layout keeps other sizes raw"),
}
Ok(())
}
/// Integers stored with a bit offset or reduced precision (N-Bit): libhdf5
/// converts them to the full-width integer of the same size, shifting the
/// value down and sign-extending from the top precision bit.
fn canon_partial_int(dt: &Datatype, b: &[u8], out: &mut Vec<u8>) -> Result<(), String> {
let Datatype::FixedPoint {
size,
byte_order,
signed,
bit_offset,
bit_precision,
} = dt
else {
unreachable!()
};
let prec = u32::from(*bit_precision);
let v = element_bits(b, byte_order)?;
let mut x = field(v, u32::from(*bit_offset), prec);
if *signed && prec > 0 && prec < 128 && field(x, prec - 1, 1) == 1 {
x |= !0u128 << prec;
}
out.extend_from_slice(&x.to_le_bytes()[..*size as usize]);
Ok(())
}
fn partial_int(dt: &Datatype) -> bool {
matches!(dt, Datatype::FixedPoint { size, bit_offset, bit_precision, .. }
if *bit_offset != 0 || u32::from(*bit_precision) != size * 8)
}
fn canon_str(b: &[u8], out: &mut Vec<u8>) {
let cut = b.iter().position(|&c| c == 0).unwrap_or(b.len());
let mut s = &b[..cut];
while let [rest @ .., b' '] = s {
s = rest;
}
out.push(b'S');
out.extend_from_slice(&(s.len() as u32).to_le_bytes());
out.extend_from_slice(s);
}
fn hex(b: &[u8]) -> String {
b.iter().map(|x| format!("{x:02x}")).collect()
}
fn dtype_str(dt: &Datatype) -> String {
match dt {
Datatype::FixedPoint {
size,
signed,
byte_order,
..
} => {
format!(
"{}{}{}",
bo(byte_order),
if *signed { "i" } else { "u" },
size
)
}
Datatype::FloatingPoint {
size, byte_order, ..
} => format!("{}f{}", bo(byte_order), size),
Datatype::BitField {
size, byte_order, ..
} => format!("{}b{}", bo(byte_order), size),
Datatype::Time { size, .. } => format!("time{size}"),
Datatype::String { size, .. } => format!("S{size}"),
Datatype::Opaque { size, .. } => format!("V{size}"),
Datatype::Compound { size, members } => format!(
"{{{}}}{size}",
members
.iter()
.map(|m| format!("{}:{}", m.name, dtype_str(&m.datatype)))
.collect::<Vec<_>>()
.join(",")
),
Datatype::Reference { ref_type, .. } => format!("ref({ref_type:?})"),
Datatype::Enumeration { base_type, .. } => format!("enum({})", dtype_str(base_type)),
Datatype::VariableLength {
is_string: true, ..
} => "vlstr".into(),
Datatype::VariableLength { base_type, .. } => format!("vlen({})", dtype_str(base_type)),
Datatype::Array {
base_type,
dimensions,
} => format!("({}){dimensions:?}", dtype_str(base_type)),
}
}
fn bo(b: &DatatypeByteOrder) -> &'static str {
match b {
DatatypeByteOrder::LittleEndian => "<",
DatatypeByteOrder::BigEndian => ">",
DatatypeByteOrder::Vax => "vax",
}
}
fn is_group(h: &ObjectHeader) -> bool {
h.messages.iter().any(|m| {
matches!(
m.msg_type,
MessageType::LinkInfo | MessageType::Link | MessageType::SymbolTable
)
})
}
fn main() {
install_hook();
let path = std::env::args().nth(1).expect("usage: probe <file>");
let mut top = Map::new();
top.insert("file".into(), Value::String(path.clone()));
let data = match std::fs::read(&path) {
Ok(d) => d,
Err(err) => {
top.insert("open_error".into(), Value::String(format!("Io({err})")));
println!("{}", Value::Object(top));
return;
}
};
// Every address is relative to the superblock: look at the file from
// there on (past any user block), as libhdf5 does.
let hdf5: &[u8] = match signature::find_signature(&data) {
Ok(off) => &data[off..],
Err(_) => &data,
};
let sb = guarded(|| Superblock::parse(hdf5, 0).map_err(e));
let sb = match sb {
Ok(sb) => sb,
Err(msg) => {
top.insert("open_error".into(), Value::String(msg));
println!("{}", Value::Object(top));
return;
}
};
top.insert("superblock_version".into(), json!(sb.version));
let ctx = Ctx {
data: hdf5,
os: sb.offset_size,
ls: sb.length_size,
base_dir: std::path::Path::new(&path)
.parent()
.map(|p| p.to_path_buf())
.unwrap_or_default(),
heaps: RefCell::new(HashMap::new()),
};
let mut objects: Vec<Value> = Vec::new();
let mut visited = HashSet::new();
let mut soft_v1 = 0u64;
// explicit DFS stack: (address, path)
let mut stack: Vec<(u64, String)> = vec![(sb.root_group_address, "/".to_string())];
while let Some((addr, p)) = stack.pop() {
if objects.len() >= MAX_OBJECTS {
top.insert("truncated".into(), json!(true));
break;
}
if !visited.insert(addr) {
continue;
}
let mut rec = Map::new();
rec.insert("path".into(), Value::String(p.clone()));
let r = guarded(|| {
let h = ctx.header(addr)?;
Ok(h)
});
let h = match r {
Ok(h) => h,
Err(msg) => {
rec.insert("kind".into(), Value::String("unknown".into()));
rec.insert("error".into(), Value::String(msg));
objects.push(Value::Object(rec));
continue;
}
};
let is_ds = h
.messages
.iter()
.any(|m| m.msg_type == MessageType::DataLayout);
let kind = if is_ds {
"dataset"
} else if is_group(&h) || addr == sb.root_group_address {
"group"
} else if h
.messages
.iter()
.any(|m| m.msg_type == MessageType::Datatype)
{
"datatype"
} else {
"unknown"
};
rec.insert("kind".into(), Value::String(kind.into()));
if kind == "dataset"
&& let Err(msg) = guarded(|| ctx.read_dataset(&h, &mut rec))
{
rec.insert("error".into(), Value::String(msg));
}
if kind != "datatype" {
match guarded(|| ctx.attrs(&h)) {
Ok(m) => {
rec.insert("attrs".into(), Value::Object(m));
}
Err(msg) => {
rec.insert("attrs_error".into(), Value::String(msg));
}
}
}
if kind == "group" {
match guarded(|| ctx.entries(&h)) {
Ok(mut ents) => {
ents.retain(|en| {
if en.cache_type == 2 {
soft_v1 += 1;
false
} else {
true
}
});
ents.sort_by(|a, b| a.name.cmp(&b.name));
let base = if p == "/" { String::new() } else { p.clone() };
for en in ents.into_iter().rev() {
stack.push((en.object_header_address, format!("{base}/{}", en.name)));
}
}
Err(msg) => {
rec.insert("list_error".into(), Value::String(msg));
}
}
}
objects.push(Value::Object(rec));
}
if soft_v1 > 0 {
top.insert("v1_soft_link_entries".into(), json!(soft_v1));
}
top.insert("objects".into(), Value::Array(objects));
println!("{}", Value::Object(top));
}
#[cfg(test)]
mod tests {
use super::*;
/// The N-Bit float of libhdf5's `test/testfiles/le_data.h5`
/// (`Nbit_float_data_le`): offset 7, precision 20, sign bit 26, exponent
/// 20+6 (bias 31), mantissa 7+13.
fn nbit_f32(byte_order: DatatypeByteOrder) -> Datatype {
Datatype::FloatingPoint {
size: 4,
byte_order,
bit_offset: 7,
bit_precision: 20,
exponent_location: 20,
exponent_size: 6,
mantissa_location: 7,
mantissa_size: 13,
exponent_bias: 31,
}
}
fn canon_one(dt: &Datatype, bytes: &[u8]) -> Vec<u8> {
let mut out = Vec::new();
canon_custom_float(dt, bytes, &mut out).unwrap();
out
}
#[test]
fn nbit_float_canonicalises_to_the_value_libhdf5_returns() {
let le = nbit_f32(DatatypeByteOrder::LittleEndian);
let be = nbit_f32(DatatypeByteOrder::BigEndian);
assert!(!ieee_layout(&le));
// 1.0: exponent = bias, mantissa 0
let one: u32 = 31 << 20;
assert_eq!(canon_one(&le, &one.to_le_bytes()), 1.0f32.to_le_bytes());
assert_eq!(canon_one(&be, &one.to_be_bytes()), 1.0f32.to_le_bytes());
// -2.1999512 (h5py's reading of the file's -2.2): sign, e = 32, m = 819
let v: u32 = (1 << 26) | (32 << 20) | (819 << 7);
assert_eq!(
canon_one(&le, &v.to_le_bytes()),
(-2.199_951_2f32).to_le_bytes()
);
assert_eq!(canon_one(&le, &[0; 4]), 0.0f32.to_le_bytes());
}
#[test]
fn ieee_floats_keep_their_raw_bytes() {
let f32le = Datatype::FloatingPoint {
size: 4,
byte_order: DatatypeByteOrder::LittleEndian,
bit_offset: 0,
bit_precision: 32,
exponent_location: 23,
exponent_size: 8,
mantissa_location: 0,
mantissa_size: 23,
exponent_bias: 127,
};
assert!(ieee_layout(&f32le));
}
#[test]
fn partial_precision_int_is_shifted_and_sign_extended() {
let dt = Datatype::FixedPoint {
size: 4,
byte_order: DatatypeByteOrder::BigEndian,
signed: true,
bit_offset: 4,
bit_precision: 17,
};
assert!(partial_int(&dt));
let stored = (((-5i32) as u32) & 0x1_FFFF) << 4;
let mut out = Vec::new();
canon_partial_int(&dt, &stored.to_be_bytes(), &mut out).unwrap();
assert_eq!(out, (-5i32).to_le_bytes());
}
}
conformance/ref.py
-259
View File
@@ -1,259 +0,0 @@
#!/usr/bin/env python3
"""Reference probe: same JSON as the Rust `conformance-probe`, produced with h5py.
Walk: iterative DFS from '/', children in sorted (UTF-8 byte) name order, hard
links only, each object once (first path wins, deduplicated by object identity).
Canonical value encoding: see harness/src/main.rs.
"""
import hashlib
import json
import os
import struct
import sys
import numpy as np
import h5py
try:
import hdf5plugin # noqa: F401 registers blosc/lz4/zstd/bzip2/... filters
except Exception: # pragma: no cover
pass
MAX_BYTES = 200 * 1024 * 1024
MAX_OBJECTS = 200_000
def canon_str(b, out):
if isinstance(b, str):
b = b.encode("utf-8", "surrogateescape")
b = bytes(b)
cut = b.find(b"\x00")
if cut >= 0:
b = b[:cut]
b = b.rstrip(b" ")
out += b"S" + struct.pack("<I", len(b)) + b
def simple(dt):
if dt.fields:
return all(simple(dt.fields[n][0]) for n in dt.names)
if dt.subdtype:
return simple(dt.subdtype[0])
return dt.kind in "iufcbV"
def packed(dt):
if dt.fields:
return np.dtype([(n, packed(dt.fields[n][0])) for n in dt.names])
if dt.subdtype:
base, shape = dt.subdtype
return np.dtype((packed(base), shape))
if dt.kind in "iufcb":
return dt.newbyteorder("<")
return dt
def canon_el(dt, val, out):
if dt.fields:
for n in dt.names:
canon_el(dt.fields[n][0], val[n], out)
return
if dt.subdtype:
base, _ = dt.subdtype
for x in np.asarray(val).reshape(-1):
canon_el(base, x, out)
return
k = dt.kind
if k in "iufcb":
out += np.asarray(val, dtype=dt).astype(dt.newbyteorder("<")).tobytes()
elif k == "V":
out += np.asarray(val, dtype=dt).tobytes()
elif k == "S":
canon_str(val, out)
elif k == "O":
if h5py.check_string_dtype(dt) is not None:
canon_str(val if val is not None else b"", out)
elif h5py.check_ref_dtype(dt) is not None:
out += b"R"
else:
base = h5py.check_vlen_dtype(dt)
if base is None:
raise TypeError(f"unhandled object dtype {dt!r}")
arr = np.asarray(val if val is not None else [], dtype=base).reshape(-1)
out += b"V" + struct.pack("<I", arr.shape[0])
if simple(base):
out += arr.astype(packed(base)).tobytes()
else:
for x in arr:
canon_el(base, x, out)
elif k == "U":
canon_str(str(val), out)
else:
raise TypeError(f"unhandled dtype kind {k} ({dt!r})")
def has_obj(dt):
if dt.fields:
return any(has_obj(dt.fields[n][0]) for n in dt.names)
if dt.subdtype:
return has_obj(dt.subdtype[0])
return dt.kind == "O"
def note_conversion(tid, dt, rec):
"""h5py converts some file types (FP8, bfloat16, x87 long double, ...) to a
different-sized numpy type; then value bytes are not comparable."""
try:
if not has_obj(dt) and tid.get_size() != dt.itemsize:
rec["converted"] = f"file type size {tid.get_size()} -> numpy {dt} ({dt.itemsize})"
except Exception: # noqa: BLE001
pass
def hash_values(arr, dt, rec):
if dt.subdtype is not None:
# h5py expands an HDF5 array element type into trailing array dims
dt = dt.subdtype[0]
arr = np.asarray(arr, dtype=dt)
if simple(dt):
c = np.ascontiguousarray(arr).astype(packed(dt)).tobytes()
else:
out = bytearray()
for x in arr.reshape(-1):
canon_el(dt, x, out)
c = bytes(out)
rec["hash"] = hashlib.sha256(c).hexdigest()
rec["head"] = c[:48].hex()
def err(e):
s = f"{type(e).__name__}: {e}"
return s.splitlines()[0][:400] if s else type(e).__name__
def shape_of(s):
return "null" if s is None else list(s)
def n_bytes(shape, tid):
n = 1
for d in shape or ():
n *= d
return n * tid.get_size()
def read_attrs(obj):
out = {}
names = sorted(obj.attrs.keys(), key=lambda s: s.encode("utf-8", "surrogateescape"))
for name in names:
rec = {}
try:
aid = obj.attrs.get_id(name)
rec["dtype"] = str(aid.dtype)
rec["shape"] = shape_of(aid.shape)
note_conversion(aid.get_type(), aid.dtype, rec)
if aid.shape is None:
hash_values(np.empty((0,), dtype=aid.dtype), aid.dtype, rec)
else:
val = obj.attrs[name]
hash_values(val, aid.dtype, rec)
except Exception as e: # noqa: BLE001
rec = {"error": err(e)}
out[name] = rec
return out
def main(path):
top = {"file": path}
try:
f = h5py.File(path, "r")
except Exception as e: # noqa: BLE001
top["open_error"] = err(e)
print(json.dumps(top))
return
objects = []
seen = set()
stack = [("/", None)]
while stack:
p, obj = stack.pop()
if len(objects) >= MAX_OBJECTS:
top["truncated"] = True
break
rec = {"path": p}
try:
if obj is None:
obj = f[p]
key = hash(obj.id) # h5py ObjectID hash = (fileno, object address/token)
except Exception as e: # noqa: BLE001
rec["kind"] = "unknown"
rec["error"] = err(e)
objects.append(rec)
continue
if key in seen:
continue
seen.add(key)
if isinstance(obj, h5py.Dataset):
kind = "dataset"
elif isinstance(obj, h5py.Group):
kind = "group"
elif isinstance(obj, h5py.Datatype):
kind = "datatype"
else:
kind = "unknown"
rec["kind"] = kind
if kind == "dataset":
try:
dt = obj.dtype
rec["dtype"] = str(dt)
rec["shape"] = shape_of(obj.shape)
note_conversion(obj.id.get_type(), dt, rec)
if obj.shape is None:
hash_values(np.empty((0,), dtype=dt), dt, rec)
elif n_bytes(obj.shape, obj.id.get_type()) > MAX_BYTES:
rec["skipped"] = "too large"
else:
arr = np.empty(obj.shape, dtype=dt)
if arr.size:
try:
obj.read_direct(arr)
except Exception: # noqa: BLE001
arr = obj[()]
hash_values(arr, dt, rec)
except Exception as e: # noqa: BLE001
rec["error"] = err(e)
if kind != "datatype":
try:
rec["attrs"] = read_attrs(obj)
except Exception as e: # noqa: BLE001
rec["attrs_error"] = err(e)
if kind == "group":
try:
names = sorted(obj.keys(), key=lambda s: s.encode("utf-8", "surrogateescape"))
base = "" if p == "/" else p
kids = []
for n in names:
try:
link = obj.get(n, getlink=True)
except Exception: # noqa: BLE001
link = None
if link is not None and not isinstance(link, h5py.HardLink):
continue
kids.append(f"{base}/{n}")
for k in reversed(kids):
stack.append((k, None))
except Exception as e: # noqa: BLE001
rec["list_error"] = err(e)
objects.append(rec)
top["objects"] = objects
print(json.dumps(top), flush=True)
# Exit without tearing down the h5py objects: freeing them for some files
# that hold references (hdf5's h5repack_attr_refs.h5, cve-2024-32623.h5)
# makes libhdf5 2.0 abort with "free(): chunks in smallbin corrupted"
# about half the time. That happens after the reading is done, so it says
# nothing about what h5py read, but it flipped those files between ok and
# h5py-cannot-read from one run to the next.
os._exit(0)
if __name__ == "__main__":
main(sys.argv[1])
conformance/report.py
-335
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@@ -1,335 +0,0 @@
#!/usr/bin/env python3
"""report.py <results_dir> <CONFORMANCE.md> <corpus_dir>
Render the sweep's results (compare.py's results.json plus the raw per-side
runs) as CONFORMANCE.md, and write <results_dir>/report-meta.json (commit,
date, versions) for check.py --update.
"""
import collections
import datetime
import json
import os
import platform
import subprocess
import sys
import h5py
import numpy
try:
import hdf5plugin
HDF5PLUGIN = hdf5plugin.version
except Exception: # noqa: BLE001
HDF5PLUGIN = "not installed"
R, OUT_MD, CORPUS = sys.argv[1], sys.argv[2], sys.argv[3]
HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.dirname(HERE)
CLASSES = ["ok", "our-error", "mismatch", "h5py-cannot-read", "panic", "hang", "crash", "oom"]
def sh(*cmd, cwd=ROOT):
try:
return subprocess.run(cmd, cwd=cwd, capture_output=True, text=True, timeout=30).stdout.strip()
except Exception: # noqa: BLE001
return ""
def cpu_model():
try:
for ln in open("/proc/cpuinfo"):
if ln.startswith(("model name", "Model")):
return ln.split(":", 1)[1].strip()
except OSError:
pass
return platform.processor() or "unknown"
def mem_gib():
try:
for ln in open("/proc/meminfo"):
if ln.startswith("MemTotal:"):
return f"{int(ln.split()[1]) / 1048576:.0f} GiB"
except OSError:
pass
return "?"
res = json.load(open(os.path.join(R, "results.json")))
meta_run = json.load(open(os.path.join(R, "meta.json"))) if os.path.exists(os.path.join(R, "meta.json")) else {}
rows = res["rows"]
issues = res.get("issues", {})
# safe.directory: a checkout owned by another user (a container) is still ours to read
commit = sh("git", "-c", "safe.directory=*", "rev-parse", "HEAD") or os.environ.get("GITHUB_SHA", "unknown")
lib_dirty = sh("git", "-c", "safe.directory=*", "status", "--porcelain", "--", "crates", "Cargo.toml")
h5dump_v = sh("h5dump", "--version").replace("h5dump: ", "")
meta = {
"date": datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d %H:%M UTC"),
"commit": commit + (" (library sources modified)" if lib_dirty else ""),
"reference": f"h5py {h5py.__version__} / HDF5 {h5py.version.hdf5_version}",
}
json.dump(meta, open(os.path.join(R, "report-meta.json"), "w"), indent=1)
pins = []
for ln in open(os.path.join(HERE, "corpus.txt")):
if ln.strip() and not ln.lstrip().startswith("#"):
name, url, rev, root, *_ = ln.split()
pins.append((name, url, rev, root))
by_corpus = collections.defaultdict(collections.Counter)
for r in rows:
by_corpus[r["corpus"]][r["class"]] += 1
total = collections.Counter(r["class"] for r in rows)
def ex_list(files, n=3):
s = ", ".join(f"`{f}`" for f in files[:n])
return s + (f" (+{len(files) - n} more)" if len(files) > n else "")
# --- known causes that are not clawhdf5 bugs --------------------------------
def is_h5py_be_vlen(i):
"""h5py returns the elements of a VL sequence of a big-endian base type
with their file (big-endian) bytes but a native-endian dtype."""
return (i["kind"] == "mismatch" and i["key"] in ("values", "attr-values")
and (i.get("ref_dtype") == "object") and (i.get("ours_dtype") or "").startswith("vlen(")
and ">" in (i.get("ours_dtype") or ""))
known = collections.defaultdict(list)
for r in rows:
if r["class"] != "mismatch":
continue
iss = issues.get(r["file"], [])
if iss and all(is_h5py_be_vlen(i) for i in iss):
known["h5py-be-vlen"].append(r["file"])
# --- the CVE corpus: clawhdf5 vs h5dump vs h5py ------------------------------
def side(run, name):
p = os.path.join(R, "runs", run, name)
if not os.path.exists(p + ".rc"):
return None
rc = int(open(p + ".rc").read().strip() or -1)
err = open(p + ".err", errors="replace").read()
try:
j = json.load(open(p + ".json"))
except Exception: # noqa: BLE001
j = None
return rc, err, j
def outcome(s, rust=False):
"""-> (bucket, text). bucket in read / error / panic / crash / hang / oom."""
if s is None:
return "missing", "not run"
rc, err, j = s
if rc in (137, 124):
return "hang", "hang (killed at timeout)"
if "memory allocation of" in err or "MemoryError" in err or "bad_alloc" in err or "Cannot allocate" in err:
return "oom", "out of memory"
if rust and (rc == 101 or "PANIC:" in err):
return "panic", "panic"
if "overflowed its stack" in err:
return "crash", "stack overflow"
if rc == 139:
return "crash", "SIGSEGV"
if rc == 134:
return "crash", "SIGABRT" + (" (heap corruption)" if ("corrupted" in err or "free()" in err) else "")
if rc > 128:
return "crash", f"signal {rc - 128}"
if j is None:
return ("error", "error exit") if rc in (0, 1) else ("crash", f"exit {rc}")
if "open_error" in j:
return "error", "open error"
objs = j.get("objects", [])
ne = sum(1 for o in objs for k in ("error", "attrs_error", "list_error") if k in o)
ne += sum(1 for o in objs for a in (o.get("attrs") or {}).values() if "error" in a)
return "read", f"read {len(objs)} obj" + (f", {ne} errors" if ne else "")
def h5dump_outcome(s):
if s is None:
return "missing", "not run"
rc, err, _ = s
if rc in (137, 124):
return "hang", "hang (killed at timeout)"
if "memory allocation" in err or "Cannot allocate" in err:
return "oom", "out of memory"
if rc == 139:
return "crash", "SIGSEGV"
if rc == 134:
return "crash", "SIGABRT" + (" (heap corruption)" if ("corrupted" in err or "free()" in err) else "")
if rc > 128:
return "crash", f"signal {rc - 128}"
return ("read", "ok") if rc == 0 else ("error", "error exit")
cve_rows = []
buckets = {"clawhdf5": collections.Counter(), "h5dump": collections.Counter(), "h5py": collections.Counter()}
ours_panic = {r["file"] for r in rows if r["class"] == "panic"}
for r in rows:
if r["corpus"] != "cve_hdf5":
continue
run = r["file"].replace("/", "__")
o = outcome(side(run, "ours"), rust=True)
if o[0] == "read" and r["file"] in ours_panic:
o = ("panic", "caught panic")
p = outcome(side(run, "ref"))
d = h5dump_outcome(side(run, "h5dump"))
buckets["clawhdf5"][o[0]] += 1
buckets["h5py"][p[0]] += 1
buckets["h5dump"][d[0]] += 1
cve_rows.append((r["file"].split("/", 1)[1], d[1], p[1], o[1], r["class"]))
# --- render -----------------------------------------------------------------
L = []
w = L.append
w("# clawhdf5 conformance report")
w("")
w("Every HDF5 file of eight public corpora (pinned by commit) is read twice — by")
w("clawhdf5 (`conformance/probe`, the same `clawhdf5-format` calls the facade")
w("makes) and by h5py/libhdf5 (`conformance/ref.py`) — and the two readings are")
w("compared object by object: the set of hard-linked objects, each dataset's and")
w("attribute's shape, and a SHA-256 of its values in a canonical encoding. The")
w("CVE corpus is also run through `h5dump`. Each side runs under a timeout and an")
w("address-space limit, so a hang, crash or runaway allocation is recorded, not")
w("fatal. This file is generated by `conformance/run.sh`; do not edit it by hand.")
w("")
w("## Run")
w("")
w("| | |")
w("|---|---|")
w(f"| date | {meta['date']} |")
w(f"| clawhdf5 commit | `{meta['commit']}` |")
w(f"| machine | `{platform.node()}`: {cpu_model()}, {os.cpu_count()} CPUs, {mem_gib()}, {platform.system()} {platform.release()} {platform.machine()} |")
w(f"| command | `{os.environ.get('CONFORMANCE_CMD', 'conformance/run.sh')}` |")
w(f"| rustc | {sh('rustc', '-V')} |")
w(f"| reference | h5py {h5py.__version__}, HDF5 {h5py.version.hdf5_version}, numpy {numpy.__version__}, hdf5plugin {HDF5PLUGIN}, Python {platform.python_version()} |")
w(f"| h5dump | {h5dump_v} (CVE corpus only) |")
if meta_run:
w(f"| limits | {meta_run.get('timeout_s')} s timeout (SIGKILL), {int(meta_run.get('mem_kb', 0)) // 1024} MiB address space, per process; {meta_run.get('jobs')} files in parallel |")
w(f"| runtime | {meta_run.get('probe_seconds')} s probing + comparing ({meta_run.get('build_seconds')} s fetch/build before it) |")
w("")
w("## Results")
w("")
w("A file's class is the first that applies:")
w("")
w("- **panic / hang / crash / oom** — clawhdf5 panicked (caught per object or not), hit the timeout, died on a signal, or failed an allocation. The CI gate fails on any of these.")
w("- **h5py-cannot-read** — libhdf5 could not open the file (or itself crashed or hung). Nothing to compare against; most are the deliberately malformed CVE reproducers.")
w("- **our-error** — clawhdf5 returned an error for something h5py reads.")
w("- **mismatch** — both read it, but the shapes, values, object set or attribute set differ.")
w("- **ok** — every object h5py reads, clawhdf5 reads identically.")
w("")
w("| corpus | files | " + " | ".join(CLASSES) + " |")
w("|---" * (len(CLASSES) + 2) + "|")
for c in sorted(by_corpus):
cnt = by_corpus[c]
w(f"| {c} | {sum(cnt.values())} | " + " | ".join(str(cnt.get(k, 0)) for k in CLASSES) + " |")
w(f"| **all** | **{len(rows)}** | " + " | ".join(f"**{total.get(k, 0)}**" for k in CLASSES) + " |")
w("")
n_known = sum(len(v) for v in known.values())
if n_known:
w(f"{n_known} of the {total.get('mismatch', 0)} mismatches are a known h5py bug, not ours (see *Known not-our-bug*).")
w("")
w("Corpora (fetched by `conformance/fetch-corpus.sh` into the gitignored `conformance/.cache/`):")
w("")
w("| corpus | source | commit |")
w("|---|---|---|")
for name, url, rev, root in pins:
w(f"| {name} | {url.removesuffix('.git')}" + ("" if root == "." else f" (`{root}`)") + f" | `{rev[:12]}` |")
w("")
w("## Panics, hangs, crashes, out-of-memory")
w("")
if not res["panics"]:
w("None.")
else:
for p in res["panics"]:
w(f"- `{p['file']}` [{p['class']}] {p['detail']}")
w("")
w("## Our-error root causes")
w("")
w("Grouped by normalised error message. *files* counts files whose class this cause affects.")
w("")
w("| files | objects | error | examples |")
w("|---:|---:|---|---|")
for k, v in res["root_causes"].items():
w(f"| {v['files']} | {v['count']} | `{k.replace('|', '/')}` | {ex_list(v['file_list'])} |")
w("")
w("## Mismatch root causes")
w("")
w("| files | objects | cause | examples |")
w("|---:|---:|---|---|")
for k, v in res["mismatch_causes"].items():
w(f"| {v['files']} | {v['count']} | `{k.replace('|', '/')}` | {ex_list(v['file_list'])} |")
w("")
w("## CVE corpus: clawhdf5 vs h5dump vs h5py")
w("")
w(f"The {len(cve_rows)} files of [HDFGroup/cve_hdf5](https://github.com/HDFGroup/cve_hdf5) — reproducers for")
w("published libhdf5 CVEs and fuzzer finds. *read* = produced output (possibly with per-object")
w("errors), *error* = refused cleanly. h5dump exits non-zero on any error anywhere in a file, so")
w("its read/error split is not comparable with the other two rows; the panic, crash, hang and oom")
w("columns are.")
w("")
w("| tool | read | error | panic | crash | hang | oom |")
w("|---|---:|---:|---:|---:|---:|---:|")
for tool, label in (("clawhdf5", "clawhdf5"), ("h5dump", f"h5dump {h5dump_v.split()[-1] if h5dump_v else ''}"),
("h5py", f"h5py {h5py.__version__} / HDF5 {h5py.version.hdf5_version}")):
b = buckets[tool]
w(f"| {label} | " + " | ".join(str(b.get(k, 0)) for k in ("read", "error", "panic", "crash", "hang", "oom")) + " |")
w("")
w("<details><summary>Per-file outcomes</summary>")
w("")
w("| file | h5dump | h5py | clawhdf5 | class |")
w("|---|---|---|---|---|")
for f, d, p, o, cls in cve_rows:
w(f"| {f} | {d} | {p} | {o} | {cls} |")
w("")
w("</details>")
w("")
w("## Known not-our-bug")
w("")
w("- **h5py big-endian variable-length sequences.** h5py returns the elements of a VL sequence")
w(" whose base type is big-endian with the file's big-endian bytes but a native (little-endian)")
w(" numpy dtype, so the values it reports are byte-swapped garbage; `h5dump` prints the values")
w(" clawhdf5 reads. Reproducer: `h5py.vlen_dtype(np.dtype('>f4'))` dataset holding `[1.0, 2.0]`")
w(" reads back in h5py as `[4.6e-41, 9.0e-44]`. Affected here: "
+ (ex_list(sorted(known["h5py-be-vlen"]), 10) if known["h5py-be-vlen"] else "none") + ".")
w("- **Non-IEEE floats and partial-precision integers (N-Bit).** libhdf5 converts a float whose")
w(" bit layout is not IEEE (e.g. `H5Tset_precision` for the N-Bit filter) or an integer with a")
w(" bit offset / reduced precision into the plain numpy type of the same size. The probe")
w(" compares such values as converted numbers, not raw file bytes (before 2026-09-25 it compared")
w(" raw bytes, which reported every N-Bit float dataset as a mismatch).")
if res["incomparable"]:
w("- **Types h5py widens.** Where h5py reads a type into a numpy type of a different size")
w(" (FP8 -> float16, bfloat16 -> float32, x87 long double -> float128) the values are not")
w(" compared (shape and presence still are): "
+ ", ".join(f"{k} ({n}x)" for k, n in res["incomparable"]) + ".")
w("- **References** are compared by presence only (`R`), not by target.")
w("")
if res.get("ref_only_errors"):
w("## Objects h5py fails on but clawhdf5 reads")
w("")
for k, n in res["ref_only_errors"][:15]:
w(f"- {n} x `{k}`")
w("")
w("## Reproduce")
w("")
w("```sh")
w("# needs: Rust, python3 with h5py numpy hdf5plugin (conformance/requirements.txt), h5dump (hdf5-tools), git")
w("CLAWHDF5_PYTHON=/path/to/venv/bin/python conformance/run.sh")
w("```")
w("")
w("The corpus (about 450 MB of sparse checkouts) is cached in `conformance/.cache/`; results for")
w("every file, both sides' raw JSON and stderr, are in `conformance/.cache/results/`.")
w("`conformance/baseline.json` holds the ok files the nightly CI job (`.gitea/workflows/conformance.yml`)")
w("must keep; `conformance/run.sh --update-baseline` rewrites it.")
with open(OUT_MD, "w") as fh:
fh.write("\n".join(L) + "\n")
conformance/requirements.txt
-6
View File
@@ -1,6 +0,0 @@
# The reference side of the conformance sweep. Pinned so the nightly job and a
# local run compare against the same libhdf5 (h5py wheels bundle it).
h5py==3.16.0
numpy==2.5.3
hdf5plugin==7.1.0
netCDF4==1.7.4
conformance/run.sh
-88
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@@ -1,88 +0,0 @@
#!/usr/bin/env bash
# conformance/run.sh — the clawhdf5 conformance sweep, end to end.
#
# fetch the pinned corpora (cached) -> build the probe -> probe every file
# with clawhdf5 and with h5py (and h5dump for the CVE corpus), each under a
# timeout and a memory limit -> compare -> write CONFORMANCE.md -> check the
# result against conformance/baseline.json.
#
# Usage: conformance/run.sh [--no-fetch] [--no-report] [--update-baseline]
#
# Environment:
# CLAWHDF5_PYTHON python with h5py, numpy, hdf5plugin (default: repo .venv, then python3)
# CONFORMANCE_CACHE corpus / build / results cache (default: conformance/.cache)
# CONFORMANCE_OUT results directory (default: $CONFORMANCE_CACHE/results)
# CONFORMANCE_REPORT report path (default: CONFORMANCE.md at the repo root)
# JOBS parallel files (default: nproc)
# CONFORMANCE_PROBE use this prebuilt probe binary instead of building one
# TMO / MEM_KB per-process timeout in seconds (20) / address-space limit in KiB (4 GiB)
#
# Exit status: 0 = gate passed; 1 = a panic/hang/crash/oom in clawhdf5, or the
# ok count fell below the baseline, or a baseline-ok file regressed; 2 = setup error.
set -euo pipefail
HERE="$(cd "$(dirname "$0")" && pwd)"
ROOT="$(cd "$HERE/.." && pwd)"
FETCH=1 REPORT=1 UPDATE=0
for a in "$@"; do
case "$a" in
--no-fetch) FETCH=0 ;;
--no-report) REPORT=0 ;;
--update-baseline) UPDATE=1 ;;
-h|--help) sed -n '2,23p' "$0"; exit 0 ;;
*) echo "unknown argument: $a" >&2; exit 2 ;;
esac
done
export PATH="$HOME/.cargo/bin:$PATH"
CACHE="${CONFORMANCE_CACHE:-$HERE/.cache}"
mkdir -p "$CACHE"; CACHE="$(cd "$CACHE" && pwd)"
OUT="${CONFORMANCE_OUT:-$CACHE/results}"
REPORT_PATH="${CONFORMANCE_REPORT:-$ROOT/CONFORMANCE.md}"
JOBS="${JOBS:-$(nproc 2>/dev/null || echo 4)}"
if [ -n "${CLAWHDF5_PYTHON:-}" ]; then PY="$CLAWHDF5_PYTHON"
elif [ -x "$ROOT/.venv/bin/python" ]; then PY="$ROOT/.venv/bin/python"
else PY="$(command -v python3)"; fi
export PY TMO="${TMO:-20}" MEM_KB="${MEM_KB:-4194304}"
command -v h5dump >/dev/null || { echo "error: h5dump not found (install hdf5-tools)" >&2; exit 2; }
"$PY" -c 'import h5py, numpy, hdf5plugin' || { echo "error: $PY lacks h5py/numpy/hdf5plugin" >&2; exit 2; }
t0=$(date +%s)
[ "$FETCH" = 1 ] && bash "$HERE/fetch-corpus.sh" "$CACHE"
C="$CACHE/corpus"
[ -d "$C" ] || { echo "error: no corpus in $C (run without --no-fetch)" >&2; exit 2; }
if [ -n "${CONFORMANCE_PROBE:-}" ]; then
export PROBE="$CONFORMANCE_PROBE" # a prebuilt probe, e.g. an older one for a before/after
else
echo "== building the probe"
CARGO_TARGET_DIR="${CARGO_TARGET_DIR:-$CACHE/target}" \
cargo build -q --release --manifest-path "$HERE/probe/Cargo.toml"
export PROBE="${CARGO_TARGET_DIR:-$CACHE/target}/release/conformance-probe"
fi
t1=$(date +%s)
rm -rf "$OUT"; mkdir -p "$OUT"
"$PY" "$HERE/list_files.py" "$C" > "$OUT/files.txt"
echo "== probing $(wc -l <"$OUT/files.txt") files, $JOBS at a time (timeout ${TMO}s, limit $((MEM_KB / 1024)) MiB)"
export C OUT HERE
# The shell's "Segmentation fault (core dumped)" notices go to probe.log; the
# signals themselves are recorded in each side's .rc.
xargs -a "$OUT/files.txt" -d '\n' -P "$JOBS" -I{} bash -c '
f="$1"; d="$OUT/runs/${f//\//__}"
case "$f" in cve_hdf5/*) export WITH_H5DUMP=1 ;; esac
"$HERE/run_one.sh" "$C/$f" "$d"' _ {} 2>"$OUT/probe.log"
echo "== comparing"
"$PY" "$HERE/compare.py" "$OUT" >/dev/null
t2=$(date +%s)
cat > "$OUT/meta.json" <<EOF
{"build_seconds": $((t1 - t0)), "probe_seconds": $((t2 - t1)), "jobs": $JOBS, "timeout_s": $TMO, "mem_kb": $MEM_KB}
EOF
export CONFORMANCE_CMD="${CONFORMANCE_CMD:-conformance/run.sh${*:+ $*}}"
if [ "$REPORT" = 1 ]; then
"$PY" "$HERE/report.py" "$OUT" "$REPORT_PATH" "$C"
echo "== wrote $REPORT_PATH"
fi
if [ "$UPDATE" = 1 ]; then
"$PY" "$HERE/check.py" "$OUT" "$HERE/baseline.json" --update
fi
"$PY" "$HERE/check.py" "$OUT" "$HERE/baseline.json"
conformance/run_one.sh
-27
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@@ -1,27 +0,0 @@
#!/usr/bin/env bash
# run_one.sh <file> <outdir>
#
# Probe one file with clawhdf5 (PROBE) and with h5py (PY ref.py), and with
# h5dump too when WITH_H5DUMP is set. Each side runs under a timeout (TMO
# seconds, SIGKILL) and an address-space limit (MEM_KB), with core dumps off.
# Writes <outdir>/<side>.{json,err,rc}; rc 137 = killed by the timeout.
set -u
f="$1"; out="$2"; mkdir -p "$out"
HERE="$(cd "$(dirname "$0")" && pwd)"
: "${PROBE:?PROBE must name the conformance-probe binary}"
: "${PY:?PY must name a python with h5py}"
TMO="${TMO:-20}"
MEM_KB="${MEM_KB:-4194304}"
run() { # name cmd...
local name=$1; shift
( ulimit -v "$MEM_KB"; ulimit -c 0; RUST_BACKTRACE=1 exec timeout -s KILL "$TMO" "$@" ) \
>"$out/$name.json" 2>"$out/$name.err"
echo $? >"$out/$name.rc"
}
run ours "$PROBE" "$f"
run ref "$PY" "$HERE/ref.py" "$f"
if [ -n "${WITH_H5DUMP:-}" ]; then
run h5dump h5dump "$f"
: >"$out/h5dump.json" # h5dump's text dump is not compared, only its exit status
fi
exit 0
crates/clawhdf5-accel/Cargo.toml
+2 -3
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@@ -1,11 +1,10 @@
[package]
name = "clawhdf5-accel"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "SIMD-accelerated operations for rustyhdf5"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
readme = "README.md"
keywords = ["hdf5", "simd", "acceleration", "performance"]
categories = ["science", "algorithms"]
crates/clawhdf5-accel/src/avx2.rs
+1 -54
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@@ -25,55 +25,6 @@ unsafe fn hsum_256(v: __m256) -> f32 {
_mm_cvtss_f32(result)
}
/// AVX2 dot product of two `i8` slices, widened to `i32`.
///
/// Each 16-byte half is sign-extended to sixteen `i16` lanes and multiplied
/// pairwise with `madd_epi16`, which sums adjacent products straight into
/// eight `i32` lanes — the widening that an autovectorised scalar loop does
/// in several shuffles is one instruction here. A pair sum is at most
/// `2 * 127 * 127`, far inside `i32`.
///
/// # Safety
/// Caller must verify is_x86_feature_detected!("avx2").
// SAFETY: Caller must have verified AVX2 via is_x86_feature_detected!.
#[target_feature(enable = "avx2")]
pub unsafe fn dot_i8(a: &[i8], b: &[i8]) -> i32 {
// SAFETY: Caller guarantees AVX2 is available per the # Safety contract;
// every load reads 32 bytes at an index checked against `len` first.
unsafe {
assert_eq!(a.len(), b.len());
let len = a.len();
let mut i = 0;
let mut acc0 = _mm256_setzero_si256();
let mut acc1 = _mm256_setzero_si256();
while i + 32 <= len {
let va = _mm256_loadu_si256(a.as_ptr().add(i).cast());
let vb = _mm256_loadu_si256(b.as_ptr().add(i).cast());
let a_lo = _mm256_cvtepi8_epi16(_mm256_castsi256_si128(va));
let b_lo = _mm256_cvtepi8_epi16(_mm256_castsi256_si128(vb));
let a_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(va, 1));
let b_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vb, 1));
acc0 = _mm256_add_epi32(acc0, _mm256_madd_epi16(a_lo, b_lo));
acc1 = _mm256_add_epi32(acc1, _mm256_madd_epi16(a_hi, b_hi));
i += 32;
}
// Horizontal sum of the eight i32 lanes.
let v = _mm256_add_epi32(acc0, acc1);
let s128 = _mm_add_epi32(_mm256_castsi256_si128(v), _mm256_extracti128_si256(v, 1));
let s64 = _mm_add_epi32(s128, _mm_unpackhi_epi64(s128, s128));
let s32 = _mm_add_epi32(s64, _mm_shuffle_epi32(s64, 0b01));
let mut sum = _mm_cvtsi128_si32(s32);
while i < len {
sum += i32::from(a[i]) * i32::from(b[i]);
i += 1;
}
sum
}
}
/// AVX2 dot product for f32 slices.
///
/// # Safety
@@ -160,11 +111,7 @@ pub unsafe fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
}
let denom = (norm_a * norm_b).sqrt();
if denom < f32::EPSILON {
0.0
} else {
dot / denom
}
if denom == 0.0 { 0.0 } else { dot / denom }
}
}
crates/clawhdf5-accel/src/avx512.rs
+1 -5
View File
@@ -89,11 +89,7 @@ pub unsafe fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
}
let denom = (norm_a * norm_b).sqrt();
if denom < f32::EPSILON {
0.0
} else {
dot / denom
}
if denom == 0.0 { 0.0 } else { dot / denom }
}
}
crates/clawhdf5-accel/src/lib.rs
+1 -124
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@@ -61,14 +61,8 @@ pub enum Backend {
Scalar,
}
/// The best available SIMD backend, detected once per process. Every kernel
/// dispatches through this, so it sits in the innermost loop of every search.
/// Detect the best available SIMD backend at runtime.
pub fn detect_backend() -> Backend {
static BACKEND: std::sync::OnceLock<Backend> = std::sync::OnceLock::new();
*BACKEND.get_or_init(detect_backend_uncached)
}
fn detect_backend_uncached() -> Backend {
#[cfg(target_arch = "aarch64")]
{
return Backend::Neon; // Always available on aarch64
@@ -122,36 +116,6 @@ pub fn dot_product(a: &[f32], b: &[f32]) -> f32 {
}
}
/// Dot product of two `i8` slices, widened to `i32`.
///
/// The kernel behind int8-quantised vector search. On x86-64 it uses the AVX2
/// path whenever AVX2 is present (including on AVX-512 machines, where it is
/// what the f32 kernels use too on a default build). On aarch64 it uses the
/// ARMv8.2 `SDOT` instruction when the CPU has the dot-product extension, and
/// plain NEON otherwise.
pub fn dot_i8(a: &[i8], b: &[i8]) -> i32 {
match detect_backend() {
#[cfg(target_arch = "aarch64")]
Backend::Neon => {
if std::arch::is_aarch64_feature_detected!("dotprod") {
// SAFETY: the dotprod extension was just detected at runtime.
unsafe { neon::dot_i8_dotprod(a, b) }
} else {
// SAFETY: NEON is always available on aarch64.
unsafe { neon::dot_i8(a, b) }
}
}
#[cfg(target_arch = "x86_64")]
// SAFETY: both variants imply AVX2 was detected at runtime (the
// AVX-512 backend is only selected on CPUs that also have AVX2).
Backend::Avx2 | Backend::Avx512 if is_x86_feature_detected!("avx2") => unsafe {
avx2::dot_i8(a, b)
},
_ => scalar::dot_i8(a, b),
}
}
/// Compute the L2 norm (magnitude) of a vector.
pub fn vector_norm(v: &[f32]) -> f32 {
dot_product(v, v).sqrt()
@@ -397,18 +361,6 @@ mod tests {
assert!(approx_eq(cosine_similarity(&a, &b), 0.0, EPSILON));
}
#[test]
fn test_cosine_near_zero_norm_clamped() {
// denom = 1e-4 * 1e-4 = 1e-8, comfortably below f32::EPSILON
// (~1.19e-7) but not exactly 0.0 — must still clamp to 0.0 so
// callers computing `1.0 - cosine_similarity(...)` treat these
// as maximally dissimilar, matching the pre-SIMD scalar guard.
let a = [1e-4f32];
let b = [1e-4f32];
assert_eq!(cosine_similarity(&a, &b), 0.0);
assert_eq!(scalar::cosine_similarity(&a, &b), 0.0);
}
#[test]
fn test_cosine_scalar_vs_dispatch() {
let a: Vec<f32> = (0..384).map(|i| (i as f32).sin()).collect();
@@ -743,78 +695,3 @@ mod tests {
}
}
}
#[cfg(test)]
mod dot_i8_tests {
use super::*;
fn codes(n: usize, seed: u64) -> Vec<i8> {
let mut state = seed;
(0..n)
.map(|_| {
state = state
.wrapping_mul(6_364_136_223_846_793_005)
.wrapping_add(1_442_695_040_888_963_407);
// Full range, including the extremes.
((state >> 56) as u8) as i8
})
.collect()
}
#[test]
fn dispatched_kernel_matches_scalar_exactly() {
// Integer arithmetic: the SIMD path must agree bit for bit, at every
// length — including ones that are not multiples of the 32-byte block,
// which exercise the tail.
for len in [0, 1, 7, 31, 32, 33, 63, 64, 100, 384, 385, 1536] {
let a = codes(len, 1 + len as u64);
let b = codes(len, 1000 + len as u64);
assert_eq!(dot_i8(&a, &b), scalar::dot_i8(&a, &b), "len {len}");
}
}
/// Dispatch only ever takes one path on a given CPU, so on a machine with
/// the dot-product extension the plain-NEON kernel would otherwise go
/// untested. Check each aarch64 kernel against scalar directly.
#[cfg(target_arch = "aarch64")]
#[test]
fn every_aarch64_kernel_matches_scalar_exactly() {
for len in [0, 1, 7, 15, 16, 17, 31, 32, 33, 63, 64, 100, 384, 385, 1536] {
let a = codes(len, 7 + len as u64);
let b = codes(len, 7000 + len as u64);
let want = scalar::dot_i8(&a, &b);
// SAFETY: NEON is always available on aarch64.
assert_eq!(unsafe { neon::dot_i8(&a, &b) }, want, "neon, len {len}");
if std::arch::is_aarch64_feature_detected!("dotprod") {
// SAFETY: the dotprod extension was just detected.
assert_eq!(
unsafe { neon::dot_i8_dotprod(&a, &b) },
want,
"dotprod, len {len}"
);
}
}
// The extremes, through both kernels.
let lo = vec![-128i8; 4096];
let hi = vec![127i8; 4096];
// SAFETY: NEON is always available on aarch64.
assert_eq!(unsafe { neon::dot_i8(&lo, &lo) }, 4096 * 128 * 128);
// SAFETY: NEON is always available on aarch64.
assert_eq!(unsafe { neon::dot_i8(&lo, &hi) }, -4096 * 128 * 127);
if std::arch::is_aarch64_feature_detected!("dotprod") {
// SAFETY: the dotprod extension was just detected.
assert_eq!(unsafe { neon::dot_i8_dotprod(&lo, &lo) }, 4096 * 128 * 128);
// SAFETY: the dotprod extension was just detected.
assert_eq!(unsafe { neon::dot_i8_dotprod(&lo, &hi) }, -4096 * 128 * 127);
}
}
#[test]
fn extremes_do_not_overflow() {
// -128 * -128 is the largest product; a long run of it must still fit.
let a = vec![-128i8; 4096];
assert_eq!(dot_i8(&a, &a), 4096 * 128 * 128);
let b = vec![127i8; 4096];
assert_eq!(dot_i8(&a, &b), -4096 * 128 * 127);
}
}
crates/clawhdf5-accel/src/neon.rs
+1 -132
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@@ -94,11 +94,7 @@ pub unsafe fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
}
let denom = (norm_a * norm_b).sqrt();
if denom < f32::EPSILON {
0.0
} else {
dot / denom
}
if denom == 0.0 { 0.0 } else { dot / denom }
}
/// NEON L2 distance.
@@ -180,130 +176,3 @@ pub fn checksum_fletcher32(data: &[u8]) -> u32 {
(sum2 << 16) | sum1
}
/// NEON dot product of two `i8` slices, widened to `i32`, for any aarch64 CPU.
///
/// `vmull_s8` multiplies eight lanes into `i16` — even `-128 * -128` is 16 384,
/// inside `i16` — and `vpadalq_s16` adds adjacent pairs of those into `i32`
/// accumulators, so nothing can overflow before the final horizontal sum.
///
/// CPUs with the ARMv8.2 dot-product extension should use
/// [`dot_i8_dotprod`], which does the multiply and the accumulate in one
/// instruction.
///
/// # Safety
/// Caller must ensure aarch64 target (NEON always available).
// SAFETY: NEON is always available on aarch64 targets; caller guarantees aarch64.
#[target_feature(enable = "neon")]
pub unsafe fn dot_i8(a: &[i8], b: &[i8]) -> i32 {
assert_eq!(a.len(), b.len());
let len = a.len();
let mut i = 0;
let mut acc0 = vdupq_n_s32(0);
let mut acc1 = vdupq_n_s32(0);
while i + 16 <= len {
// SAFETY: NEON is available per the # Safety contract, and both
// 16-byte loads start at an index checked against `len` above.
unsafe {
let va = vld1q_s8(a.as_ptr().add(i));
let vb = vld1q_s8(b.as_ptr().add(i));
acc0 = vpadalq_s16(acc0, vmull_s8(vget_low_s8(va), vget_low_s8(vb)));
acc1 = vpadalq_s16(acc1, vmull_high_s8(va, vb));
}
i += 16;
}
let mut sum = vaddvq_s32(vaddq_s32(acc0, acc1));
while i < len {
sum += i32::from(a[i]) * i32::from(b[i]);
i += 1;
}
sum
}
/// One `SDOT`: for each of the four `i32` lanes of `acc`, add the dot
/// product of the corresponding four `i8` pairs from `a` and `b`.
///
/// Written as inline assembly because the `vdotq_s32` intrinsic is still
/// behind the unstable `stdarch_neon_dotprod` feature; inline assembly is
/// stable on aarch64.
///
/// # Safety
/// Caller must ensure the CPU supports the `dotprod` extension.
#[inline]
#[target_feature(enable = "neon,dotprod")]
unsafe fn sdot(acc: int32x4_t, a: int8x16_t, b: int8x16_t) -> int32x4_t {
let mut acc = acc;
// SAFETY: `dotprod` is enabled for this function and the caller
// guarantees the CPU supports it. The instruction reads only its three
// vector registers and touches no memory.
unsafe {
std::arch::asm!(
"sdot {acc:v}.4s, {a:v}.16b, {b:v}.16b",
acc = inout(vreg) acc,
a = in(vreg) a,
b = in(vreg) b,
options(pure, nomem, nostack),
);
}
acc
}
/// NEON dot product of two `i8` slices using the ARMv8.2 dot-product
/// extension (`SDOT`): sixteen multiply-accumulates per instruction, straight
/// into `i32` lanes.
///
/// Present on the cores this crate actually runs on — Cortex-A76 and later
/// (Raspberry Pi 5, current Android phones), Neoverse-N1 (Graviton2, Ampere
/// Altra), and every Apple Silicon generation.
///
/// # Safety
/// Caller must verify `is_aarch64_feature_detected!("dotprod")`.
// SAFETY: caller has verified the dotprod extension at runtime.
#[target_feature(enable = "neon,dotprod")]
pub unsafe fn dot_i8_dotprod(a: &[i8], b: &[i8]) -> i32 {
assert_eq!(a.len(), b.len());
let len = a.len();
let mut i = 0;
let mut acc0 = vdupq_n_s32(0);
let mut acc1 = vdupq_n_s32(0);
// Two independent accumulators so consecutive SDOTs are not serialised on
// one register.
while i + 32 <= len {
// SAFETY: dotprod is available per the # Safety contract, and every
// 16-byte load starts at an index checked against `len` above.
unsafe {
acc0 = sdot(
acc0,
vld1q_s8(a.as_ptr().add(i)),
vld1q_s8(b.as_ptr().add(i)),
);
acc1 = sdot(
acc1,
vld1q_s8(a.as_ptr().add(i + 16)),
vld1q_s8(b.as_ptr().add(i + 16)),
);
}
i += 32;
}
if i + 16 <= len {
// SAFETY: as above; the load is bounds-checked by this condition.
unsafe {
acc0 = sdot(
acc0,
vld1q_s8(a.as_ptr().add(i)),
vld1q_s8(b.as_ptr().add(i)),
);
}
i += 16;
}
let mut sum = vaddvq_s32(vaddq_s32(acc0, acc1));
while i < len {
sum += i32::from(a[i]) * i32::from(b[i]);
i += 1;
}
sum
}
crates/clawhdf5-accel/src/scalar.rs
+1 -35
View File
@@ -21,11 +21,7 @@ pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
norm_b += y * y;
}
let denom = (norm_a * norm_b).sqrt();
if denom < f32::EPSILON {
0.0
} else {
dot / denom
}
if denom == 0.0 { 0.0 } else { dot / denom }
}
pub fn batch_cosine(query: &[f32], vectors: &[&[f32]], results: &mut [(usize, f32)]) {
@@ -140,33 +136,3 @@ fn f16_to_f32_soft(h: u16) -> f32 {
f32::from_bits(f32_bits)
}
/// Dot product of two `i8` slices, widened to `i32`.
///
/// `dim` terms of at most `127 * 127` fit an `i32` for any realistic
/// dimension (over 130 000 terms before overflow is possible).
pub fn dot_i8(a: &[i8], b: &[i8]) -> i32 {
assert_eq!(a.len(), b.len());
// Four independent accumulators over 32-lane blocks: the widening product
// has to sit in a fixed-length chunk for the vectoriser to see it, and the
// separate accumulators keep it off one dependency chain.
const LANE: usize = 8;
let (a_blocks, a_tail) = a.as_chunks::<{ LANE * 4 }>();
let (b_blocks, b_tail) = b.as_chunks::<{ LANE * 4 }>();
let mut acc = [0i32; 4];
for (x, y) in a_blocks.iter().zip(b_blocks) {
for (lane, slot) in acc.iter_mut().enumerate() {
let mut sum = 0i32;
for k in 0..LANE {
sum += i32::from(x[lane * LANE + k]) * i32::from(y[lane * LANE + k]);
}
*slot += sum;
}
}
let tail: i32 = a_tail
.iter()
.zip(b_tail)
.map(|(&x, &y)| i32::from(x) * i32::from(y))
.sum();
acc[0] + acc[1] + acc[2] + acc[3] + tail
}
crates/clawhdf5-agent/Cargo.toml
+11 -24
View File
@@ -1,28 +1,23 @@
[package]
name = "clawhdf5-agent"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "HDF5-backed persistent memory store for on-device AI agents"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
readme = "README.md"
keywords = ["agent", "memory", "hdf5", "vector-search", "embedding"]
categories = ["database", "science", "algorithms"]
[dependencies]
clawhdf5-format = { path = "../clawhdf5-format", version = "2.7.0", features = ["parallel", "fast-checksum"] }
clawhdf5 = { path = "../clawhdf5", version = "2.7.0" }
clawhdf5-io = { path = "../clawhdf5-io", version = "2.7.0", features = ["mmap"] }
clawhdf5-accel = { path = "../clawhdf5-accel", version = "2.7.0" }
clawhdf5-ann = { path = "../clawhdf5-ann", version = "2.7.0", optional = true }
clawhdf5-gpu = { path = "../clawhdf5-gpu", version = "2.7.0", optional = true, default-features = false }
clawhdf5-format = { path = "../clawhdf5-format", version = "2.1.0", features = ["parallel", "fast-checksum"] }
clawhdf5 = { path = "../clawhdf5", version = "2.1.0" }
clawhdf5-io = { path = "../clawhdf5-io", version = "2.1.0", features = ["mmap"] }
clawhdf5-accel = { path = "../clawhdf5-accel", version = "2.1.0" }
clawhdf5-ann = { path = "../clawhdf5-ann", version = "2.1.0", optional = true }
clawhdf5-gpu = { path = "../clawhdf5-gpu", version = "2.1.0", optional = true, default-features = false }
serde = { workspace = true }
byteorder = "1"
# Signed checkpoints (MemoryConfig-independent; see `signing`). Pure Rust.
ed25519-dalek = { version = "2", features = ["rand_core"] }
sha2 = "0.10"
rand_core = { version = "0.6", features = ["getrandom"] }
half = { workspace = true, optional = true }
rayon = { version = "1", optional = true }
matrixmultiply = { version = "0.3", optional = true }
@@ -49,25 +44,17 @@ harness = false
name = "memory_bench"
harness = false
[[bench]]
name = "multimodal_bench"
harness = false
[features]
default = ["float16", "hnsw", "parallel"]
default = ["float16", "hnsw"]
float16 = ["half"]
# Rayon-parallel brute-force search strategies, and a parallel bulk build of
# the HNSW index (same graph, several times faster on a multi-core machine).
parallel = ["rayon", "clawhdf5-ann?/parallel"]
# Compress embeddings with Zstd instead of deflate when
# `MemoryConfig::compression` is on. Off by default: it links libzstd (C).
zstd = ["clawhdf5/zstd"]
parallel = ["rayon"]
# HNSW approximate-nearest-neighbour acceleration for the vector stage of
# hybrid_search. On by default; the index is rebuilt from the cache on demand
# and stays self-consistent with the persisted memory store. Disable with
# `--no-default-features` (plus re-enabling other defaults) to force the exact
# linear cosine scan.
hnsw = ["clawhdf5-ann"]
agent = []
gpu = ["clawhdf5-gpu/gpu-wgpu"]
fast-math = ["matrixmultiply"]
accelerate = ["accelerate-src", "cblas-sys"]
crates/clawhdf5-agent/benches/bench.rs
+3 -16
View File
@@ -483,7 +483,7 @@ fn rayon_benches(c: &mut Criterion) {
use rayon::prelude::*;
let query_norm = vector_search::compute_norm(&query);
let num_cores = rayon::current_num_threads().max(1);
let chunk_size = n.div_ceil(num_cores);
let chunk_size = (n + num_cores - 1) / num_cores;
let mut results: Vec<(usize, f32)> = vectors
.par_chunks(chunk_size)
.enumerate()
@@ -537,7 +537,7 @@ fn rayon_benches(c: &mut Criterion) {
use rayon::prelude::*;
let query_norm = vector_search::compute_norm(&query);
let num_cores = rayon::current_num_threads().max(1);
let chunk_size = n.div_ceil(num_cores);
let chunk_size = (n + num_cores - 1) / num_cores;
let mut results: Vec<(usize, f32)> = vectors
.par_chunks(chunk_size)
.enumerate()
@@ -766,22 +766,12 @@ fn adaptive_benches(c: &mut Criterion) {
.map(|v| vector_search::compute_norm(v))
.collect();
let tombstones = vec![0u8; n];
let flat: Vec<f32> = vectors.iter().flatten().copied().collect();
c.bench_function("adaptive_search_10k", |b| {
let hw = HardwareCapabilities::detect();
let strat = strategy::auto_select_strategy(n, &hw);
b.iter(|| {
strategy::search_with_metrics(
&query,
&vectors,
&flat,
&norms,
&tombstones,
10,
strat,
None,
)
strategy::search_with_metrics(&query, &vectors, &norms, &tombstones, 10, strat, None)
});
});
@@ -791,7 +781,6 @@ fn adaptive_benches(c: &mut Criterion) {
strategy::search_with_metrics(
&query,
&vectors,
&flat,
&norms,
&tombstones,
10,
@@ -806,7 +795,6 @@ fn adaptive_benches(c: &mut Criterion) {
strategy::search_with_metrics(
&query,
&vectors,
&flat,
&norms,
&tombstones,
10,
@@ -821,7 +809,6 @@ fn adaptive_benches(c: &mut Criterion) {
strategy::search_with_metrics(
&query,
&vectors,
&flat,
&norms,
&tombstones,
10,
crates/clawhdf5-agent/benches/memory_bench.rs
+6 -19
View File
@@ -1,7 +1,6 @@
use clawhdf5_agent::bm25::BM25Index;
use clawhdf5_agent::consolidation::{
ConsolidationConfig, ConsolidationEngine, ImportanceScorer, ImportanceWeights, MemorySource,
UntrustedSource,
};
use clawhdf5_agent::hybrid::{hybrid_search, rrf_hybrid_search};
use clawhdf5_agent::knowledge::KnowledgeCache;
@@ -286,12 +285,7 @@ fn consolidation_benches(c: &mut Criterion) {
for i in 0..n {
let embedding = make_vec(&mut rng, DIM);
let chunk = format!("memory record {i} with some content");
engine.add_memory(
chunk,
embedding,
UntrustedSource::User,
now + i as f64,
);
engine.add_memory(chunk, embedding, MemorySource::User, now + i as f64);
}
engine
},
@@ -313,10 +307,9 @@ fn consolidation_benches(c: &mut Criterion) {
for i in 0..50usize {
let embedding = make_vec(&mut rng, DIM);
let chunk = format!("existing record {i}");
engine.add_memory(chunk, embedding, UntrustedSource::User, now + i as f64);
engine.add_memory(chunk, embedding, MemorySource::User, now + i as f64);
}
let records = engine.records().to_vec();
let record_refs: Vec<&_> = records.iter().collect();
let weights = ImportanceWeights::default();
let query_embedding = make_vec(&mut rng, DIM);
let sample_text =
@@ -324,7 +317,7 @@ fn consolidation_benches(c: &mut Criterion) {
group.bench_function("bench_importance_scoring", |b| {
b.iter(|| {
let surprise = ImportanceScorer::score_surprise(&query_embedding, &record_refs);
let surprise = ImportanceScorer::score_surprise(&query_embedding, &records);
let correction = ImportanceScorer::score_correction(&MemorySource::Correction);
let length = ImportanceScorer::score_length(sample_text);
ImportanceScorer::score_combined(surprise, correction, length, &weights)
@@ -361,7 +354,7 @@ fn temporal_benches(c: &mut Criterion) {
// Insert benchmark: measure time to insert 10k timestamps one by one
group.bench_function("bench_temporal_insert_10k", |b| {
b.iter_batched(
TemporalIndex::new,
|| TemporalIndex::new(),
|mut idx| {
for i in 0..N {
// Shuffle insertion order slightly using a simple offset pattern
@@ -449,8 +442,7 @@ fn large_consolidation_benches(c: &mut Criterion) {
let mut group = c.benchmark_group("consolidation_large");
group.sample_size(10);
{
let (label, n) = ("10k", 10_000usize);
for (label, n) in [("10k", 10_000usize)] {
group.bench_with_input(
BenchmarkId::new("bench_consolidation_cycle", label),
&n,
@@ -467,12 +459,7 @@ fn large_consolidation_benches(c: &mut Criterion) {
for i in 0..n {
let embedding = make_vec(&mut rng, DIM);
let chunk = format!("memory record {i} with content");
engine.add_memory(
chunk,
embedding,
UntrustedSource::User,
now + i as f64,
);
engine.add_memory(chunk, embedding, MemorySource::User, now + i as f64);
}
engine
},
crates/clawhdf5-agent/benches/multimodal_bench.rs
-107
View File
@@ -1,107 +0,0 @@
//! Multi-modal memory search benchmarks (`clawhdf5_agent::multimodal`).
//!
//! Covers `MultiModalStore::search_cross_modal` (every embedding of every
//! record, whatever its modality) and, for comparison,
//! `MultiModalStore::search_by_modality` restricted to one modality.
//!
//! Corpus: N records (1K and 10K), each carrying two 384-dim embeddings —
//! a text embedding of its caption plus one embedding of its primary modality,
//! cycling Image / Audio / Video — so a cross-modal query scores 2N vectors.
//! All data comes from a fixed-seed LCG, so every run sees the same corpus.
//!
//! Run: `cargo bench -p clawhdf5-agent --bench multimodal_bench`
use std::collections::HashMap;
use clawhdf5_agent::multimodal::{
MediaRef, ModalEmbedding, Modality, MultiModalRecord, MultiModalStore,
};
use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main};
// ---------------------------------------------------------------------------
// Simple deterministic PRNG (LCG), same as the other agent benches
// ---------------------------------------------------------------------------
struct Rng(u32);
impl Rng {
fn new(seed: u32) -> Self {
Self(seed)
}
fn next_u32(&mut self) -> u32 {
self.0 = self.0.wrapping_mul(1103515245).wrapping_add(12345);
self.0 >> 16
}
fn next_f32(&mut self) -> f32 {
self.next_u32() as f32 / 65536.0 - 0.5
}
}
fn make_vec(rng: &mut Rng, dim: usize) -> Vec<f32> {
(0..dim).map(|_| rng.next_f32()).collect()
}
// ---------------------------------------------------------------------------
// Corpus
// ---------------------------------------------------------------------------
const DIM: usize = 384;
const K: usize = 10;
const MEDIA: [(Modality, &str, &str); 3] = [
(Modality::Image, "image/png", "clip-vit-base"),
(Modality::Audio, "audio/wav", "clap-base"),
(Modality::Video, "video/mp4", "xclip-base"),
];
fn build_store(n: usize, seed: u32) -> MultiModalStore {
let mut rng = Rng::new(seed);
let mut store = MultiModalStore::new();
for i in 0..n {
let (modality, mime, model) = &MEDIA[i % MEDIA.len()];
let embeddings = vec![
ModalEmbedding::new(Modality::Text, make_vec(&mut rng, DIM), "minilm-l6"),
ModalEmbedding::new(modality.clone(), make_vec(&mut rng, DIM), *model),
];
store.add_record(MultiModalRecord {
id: 0,
primary_modality: modality.clone(),
text_content: Some(format!("{modality} memory {i}")),
media_ref: Some(MediaRef::path(format!("/media/{i}"), *mime)),
embeddings,
observation: None,
timestamp: 1_700_000_000.0 + i as f64,
metadata: HashMap::new(),
});
}
store
}
// ---------------------------------------------------------------------------
// Benchmarks
// ---------------------------------------------------------------------------
fn multimodal_search_benches(c: &mut Criterion) {
let query = make_vec(&mut Rng::new(99), DIM);
let mut group = c.benchmark_group("multimodal_search");
group.sample_size(50);
for (label, n) in [("1k", 1_000usize), ("10k", 10_000)] {
let store = build_store(n, 42);
assert_eq!(store.count(), n);
group.bench_with_input(BenchmarkId::new("cross_modal", label), &n, |b, _| {
b.iter(|| store.search_cross_modal(&query, K));
});
group.bench_with_input(BenchmarkId::new("by_modality_image", label), &n, |b, _| {
b.iter(|| store.search_by_modality(&Modality::Image, &query, K));
});
}
group.finish();
}
criterion_group!(multimodal_benches, multimodal_search_benches);
criterion_main!(multimodal_benches);
crates/clawhdf5-agent/fuzz/.gitignore
-3
View File
@@ -1,3 +0,0 @@
target/
artifacts/
coverage/
crates/clawhdf5-agent/fuzz/Cargo.toml
-23
View File
@@ -1,23 +0,0 @@
[package]
name = "clawhdf5-agent-fuzz"
version = "0.0.0"
publish = false
edition = "2024"
[package.metadata]
cargo-fuzz = true
[dependencies]
libfuzzer-sys = "0.4"
tempfile = "3"
[dependencies.clawhdf5-agent]
path = ".."
[workspace]
members = ["."]
[[bin]]
name = "fuzz_wal_replay"
path = "fuzz_targets/fuzz_wal_replay.rs"
doc = false
crates/clawhdf5-agent/fuzz/fuzz_targets/fuzz_wal_replay.rs
-36
View File
@@ -1,36 +0,0 @@
#![no_main]
//! Arbitrary bytes as a WAL file. Reading, and opening for append (which scans
//! the chain and truncates an unverifiable tail), must never panic, hang, or
//! allocate without bound — and after `open` repairs the file, everything
//! `read_entries` returned before must still be returned.
//!
//! The deterministic counterpart that runs in ordinary CI is
//! `tests/wal_properties.rs`; this target explores inputs it cannot reach.
use std::io::Write as _;
use clawhdf5_agent::wal::WalFile;
use libfuzzer_sys::fuzz_target;
fuzz_target!(|data: &[u8]| {
let Ok(mut tmp) = tempfile::NamedTempFile::new() else {
return;
};
if tmp.write_all(data).and_then(|()| tmp.flush()).is_err() {
return;
}
let before = WalFile::read_entries(tmp.path()).map(|e| e.len());
// Only the chained formats (header versions 3 and 4) are repaired in
// place. `open` deliberately recreates a legacy-format file from scratch:
// `HDF5Memory::open` has already replayed its entries by then.
let chained = matches!(data.get(4), Some(3 | 4));
let opened = WalFile::open(tmp.path());
if !chained {
return;
}
if let (Ok(before), Ok(wal)) = (before, opened) {
drop(wal);
let after = WalFile::read_entries(tmp.path()).map(|e| e.len());
assert_eq!(after.ok(), Some(before), "open() changed what is replayable");
}
});
crates/clawhdf5-agent/src/agents_md.rs
-4
View File
@@ -118,10 +118,6 @@ mod tests {
created_at: "2025-01-01T00:00:00Z".to_string(),
wal_enabled: false,
wal_max_entries: 500,
quantized_index: false,
hnsw_m: 16,
hnsw_ef_construction: 64,
hnsw_ef_search: 0,
}
}
crates/clawhdf5-agent/src/anomaly.rs
+5 -243
View File
@@ -82,68 +82,6 @@ impl Default for AnomalyConfig {
}
}
// ---------------------------------------------------------------------------
// Pattern-match normalization
// ---------------------------------------------------------------------------
/// `true` for characters used to invisibly break up text without being
/// rendered (zero-width joiners/spacers, bidi control marks, the BOM/ZWNBSP,
/// soft hyphen, and the invisible math operators) — a common trick for
/// splitting a flagged word so a literal-substring check misses it while the
/// text still displays normally.
fn is_invisible_format_char(ch: char) -> bool {
matches!(
ch,
'\u{00AD}' // soft hyphen
| '\u{200B}' // zero width space
| '\u{200C}' // zero width non-joiner
| '\u{200D}' // zero width joiner
| '\u{200E}' // left-to-right mark
| '\u{200F}' // right-to-left mark
| '\u{2060}' // word joiner
| '\u{2061}'..='\u{2064}' // invisible times/plus/separator/function application
| '\u{202A}'..='\u{202E}' // bidi embedding/override controls
| '\u{FEFF}' // BOM / zero width no-break space
)
}
/// Normalize text before suspicious-pattern matching so the cheapest evasion
/// tricks — extra whitespace, zero-width characters, or punctuation spliced
/// between letters (e.g. `"s.y.s.t.e.m"`) — don't defeat a literal-substring
/// check. Lowercases, drops invisible-format and control characters, drops
/// punctuation entirely (not just collapses it, so split words rejoin), and
/// collapses whitespace runs to a single space.
///
/// Does not perform Unicode NFKC normalization or confusable/homoglyph
/// folding (see [`WriteAnomalyDetector::check_pattern_anomaly`]).
fn normalize_for_pattern_match(text: &str) -> String {
let mut out = String::with_capacity(text.len());
let mut last_was_space = true; // trims leading whitespace for free
for ch in text.chars() {
if ch.is_control() || is_invisible_format_char(ch) {
continue;
}
if ch.is_whitespace() {
if !last_was_space {
out.push(' ');
last_was_space = true;
}
continue;
}
if ch.is_ascii_punctuation() {
continue;
}
for lower in ch.to_lowercase() {
out.push(lower);
}
last_was_space = false;
}
while out.ends_with(' ') {
out.pop();
}
out
}
// ---------------------------------------------------------------------------
// WriteEvent
// ---------------------------------------------------------------------------
@@ -161,9 +99,6 @@ pub struct WriteEvent {
// WriteAnomalyDetector
// ---------------------------------------------------------------------------
/// Upper bound on distinct session ids the detector tracks at once.
const MAX_TRACKED_SESSIONS: usize = 4096;
/// Tracks write events and raises alerts for suspicious behaviour.
#[derive(Debug)]
pub struct WriteAnomalyDetector {
@@ -192,23 +127,6 @@ impl WriteAnomalyDetector {
if event.timestamp > self.last_timestamp {
self.last_timestamp = event.timestamp;
}
// Bound the per-session map: a long-lived process sees an unbounded
// number of distinct session ids. When it overflows, forget the
// sessions with the fewest writes (they are furthest from the limit
// this map exists to enforce); the current one is re-added below.
if self.session_counts.len() >= MAX_TRACKED_SESSIONS
&& !self.session_counts.contains_key(&event.session_id)
{
let mut counts: Vec<u32> = self.session_counts.values().copied().collect();
let keep_from = counts.len() / 2;
counts.select_nth_unstable(keep_from);
let threshold = counts[keep_from];
self.session_counts.retain(|_, c| *c >= threshold);
if self.session_counts.len() >= MAX_TRACKED_SESSIONS {
// Every session had the same count: drop them all.
self.session_counts.clear();
}
}
*self
.session_counts
.entry(event.session_id.clone())
@@ -228,13 +146,6 @@ impl WriteAnomalyDetector {
/// Returns an alert if the number of writes in the last 60 seconds exceeds
/// `config.max_writes_per_minute`, or if any session has exceeded
/// `config.max_writes_per_session`.
///
/// The 60-second window is a single shared window across all
/// sessions/sources, so when it trips the alert additionally names the
/// top-contributing session and source within that window — a session
/// can never account for more of the window than the aggregate count, so
/// this attributes the same trip to its actual offender rather than
/// reporting only the anonymous aggregate total.
pub fn check_rate_anomaly(&self) -> Option<AnomalyAlert> {
let recent = self.window.len() as u32;
if recent > self.config.max_writes_per_minute {
@@ -245,31 +156,11 @@ impl WriteAnomalyDetector {
} else {
Severity::Medium
};
let mut per_session: std::collections::HashMap<&str, u32> =
std::collections::HashMap::new();
// MemorySource isn't Eq/Hash, so key by its Display string instead.
let mut per_source: std::collections::HashMap<String, u32> =
std::collections::HashMap::new();
for e in &self.window {
*per_session.entry(e.session_id.as_str()).or_insert(0) += 1;
*per_source.entry(e.source.to_string()).or_insert(0) += 1;
}
let top_session = per_session.iter().max_by_key(|&(_, &c)| c);
let top_source = per_source.iter().max_by_key(|&(_, &c)| c);
let attribution = match (top_session, top_source) {
(Some((session, s_count)), Some((source, r_count))) => format!(
"; top contributor: session '{session}' with {s_count} writes, \
source {source} with {r_count} writes"
),
_ => String::new(),
};
return Some(AnomalyAlert {
severity,
message: format!(
"Rate limit exceeded: {} writes in last 60s (max {}){}",
recent, self.config.max_writes_per_minute, attribution
"Rate limit exceeded: {} writes in last 60s (max {})",
recent, self.config.max_writes_per_minute
),
timestamp: self.last_timestamp,
});
@@ -297,24 +188,11 @@ impl WriteAnomalyDetector {
// -----------------------------------------------------------------------
/// Returns an alert if `chunk` contains any of the configured suspicious
/// patterns, after normalizing both sides to defeat the cheapest evasion
/// tricks (case, extra whitespace, punctuation between letters,
/// zero-width/invisible-formatting characters).
///
/// This does not perform Unicode NFKC normalization or confusable/
/// homoglyph folding (e.g. Cyrillic 'а' standing in for Latin 'a') —
/// that needs a per-codepoint confusable table (Unicode's
/// `confusables.txt`) beyond what's practical to hand-roll correctly,
/// and no such crate is a dependency of this crate today. A determined
/// attacker using homoglyphs can still evade these patterns.
/// patterns (case-insensitive).
pub fn check_pattern_anomaly(&self, chunk: &str) -> Option<AnomalyAlert> {
let normalized = normalize_for_pattern_match(chunk);
let lower = chunk.to_lowercase();
for pattern in &self.config.suspicious_patterns {
let normalized_pattern = normalize_for_pattern_match(pattern);
if normalized_pattern.is_empty() {
continue;
}
if normalized.contains(&normalized_pattern) {
if lower.contains(pattern.as_str()) {
let severity = if pattern.contains("ignore") || pattern.contains("override") {
Severity::Critical
} else if pattern.contains("system") || pattern.contains("jailbreak") {
@@ -449,57 +327,6 @@ mod tests {
assert!(alert.unwrap().severity >= Severity::Medium);
}
/// A single session dominating the shared 60s window must be named in
/// the alert, not just the anonymous aggregate count — this is the case
/// the separate cumulative max_writes_per_session check doesn't cover
/// (the window can trip before the session's lifetime total does).
#[test]
fn rate_anomaly_names_offending_session() {
let mut det = WriteAnomalyDetector::new(cfg());
for i in 0..11 {
det.record_write(event(
1.0 + i as f64 * 0.1,
"flood-session",
MemorySource::User,
));
}
let alert = det.check_rate_anomaly().unwrap();
assert!(
alert.message.contains("flood-session"),
"expected the offending session to be named, got: {}",
alert.message
);
}
/// When many distinct sessions jointly trip the shared window, the top
/// contributor named must actually be the one with the most writes.
#[test]
fn rate_anomaly_attributes_top_contributor_among_many_sessions() {
let mut det = WriteAnomalyDetector::new(cfg());
// 5 sessions with 1 write each (below any per-session limit)...
for i in 0..5 {
det.record_write(event(
1.0 + i as f64 * 0.1,
"minor-session",
MemorySource::User,
));
}
// ...plus one session responsible for the majority of the flood.
for i in 0..8 {
det.record_write(event(
2.0 + i as f64 * 0.1,
"major-session",
MemorySource::User,
));
}
let alert = det.check_rate_anomaly().unwrap();
assert!(
alert.message.contains("major-session"),
"expected the top contributor to be named, got: {}",
alert.message
);
}
#[test]
fn rate_anomaly_critical_3x() {
let mut det = WriteAnomalyDetector::new(cfg());
@@ -568,71 +395,6 @@ mod tests {
assert!(alert.is_some());
}
// --- Pattern-match evasion hardening ---
#[test]
fn pattern_defeats_extra_whitespace() {
let det = WriteAnomalyDetector::new(cfg());
let alert = det.check_pattern_anomaly("please ignore previous instructions");
assert!(alert.is_some(), "extra whitespace must not defeat matching");
}
#[test]
fn pattern_defeats_punctuation_splicing() {
let det = WriteAnomalyDetector::new(cfg());
let alert = det.check_pattern_anomaly("i.g.n.o.r.e p-r-e-v-i-o-u-s instructions");
assert!(
alert.is_some(),
"punctuation spliced between letters must not defeat matching"
);
}
#[test]
fn pattern_defeats_zero_width_space() {
let det = WriteAnomalyDetector::new(cfg());
// Zero-width space (U+200B) inserted mid-word.
let chunk = "ign\u{200B}ore previ\u{200B}ous instructions";
let alert = det.check_pattern_anomaly(chunk);
assert!(
alert.is_some(),
"zero-width space injection must not defeat matching"
);
}
#[test]
fn pattern_defeats_zero_width_joiner_and_bom() {
let det = WriteAnomalyDetector::new(cfg());
let chunk = "jail\u{200D}break\u{FEFF} attempt";
let alert = det.check_pattern_anomaly(chunk);
assert!(
alert.is_some(),
"ZWJ/BOM injection must not defeat matching"
);
}
#[test]
fn pattern_still_clean_after_normalization() {
let det = WriteAnomalyDetector::new(cfg());
// Normalization must not introduce false positives on ordinary text
// that merely contains punctuation and extra whitespace.
let alert =
det.check_pattern_anomaly("Well, I think... the weather is nice today, right?");
assert!(alert.is_none());
}
#[test]
fn normalize_for_pattern_match_examples() {
assert_eq!(
normalize_for_pattern_match("i.g.n.o.r.e p-r-e-v-i-o-u-s"),
"ignore previous"
);
assert_eq!(
normalize_for_pattern_match("ign\u{200B}ore previous"),
"ignore previous"
);
assert_eq!(normalize_for_pattern_match("SYSTEM:"), "system");
}
#[test]
fn pattern_jailbreak() {
let det = WriteAnomalyDetector::new(cfg());
crates/clawhdf5-agent/src/async_memory.rs
+1 -5
View File
@@ -37,7 +37,7 @@
//! let mem = AsyncHDF5Memory::open_with(path, config).await?;
//! mem.save(entry).await?; // buffered → background writer
//! mem.save_batch(entries).await?; // also buffered
//! let results = mem.hybrid_search(emb, "query".into(), 0.4, 0.6, 5).await;
//! let results = mem.hybrid_search(emb, "query".into(), 0.7, 0.3, 5).await;
//! mem.shutdown().await?; // final flush + stop
//! ```
@@ -408,10 +408,6 @@ impl AsyncHDF5Memory {
let (tx, rx) = oneshot::channel();
let _ = self.write_tx.send(WriteCmd::Shutdown(tx)).await;
let _ = rx.await;
// The writer task has stopped, so nothing can write through this
// handle any more: release the single-writer lock now rather than at
// drop, so the store can be reopened while `self` is still in scope.
self.inner.lock().await.release_store_lock();
Ok(())
}
}
crates/clawhdf5-agent/src/bm25.rs
+120 -414
View File
@@ -3,38 +3,12 @@
//! Provides a standard BM25 (Okapi BM25) implementation with an in-memory
//! inverted index. Tombstoned documents are excluded from indexing and search.
//!
//! The index is **incremental**: [`BM25Index::add_document`] and
//! [`BM25Index::remove_document`] keep it exactly equivalent to one built from
//! scratch over the same live documents, so a store can maintain one index for
//! its lifetime instead of re-tokenising the whole corpus per query. To make
//! that possible IDF is computed at query time (it depends on the live
//! document count) rather than cached at build time.
//!
//! - Posting lists sorted by doc id
//! - Bounded-heap top-k; results ordered by score, then doc id (deterministic)
//! Optimizations:
//! - Cached IDF scores (don't recompute per query)
//! - Sorted posting lists by doc_id for cache-friendly access
//! - Block-Max WAND early termination
use std::cmp::Reverse;
use std::collections::{BinaryHeap, HashMap};
/// `f32` wrapper providing a total order (via `total_cmp`) so BM25 scores can
/// be kept in a `BinaryHeap`. Scores are always finite in practice (no NaN
/// inputs reach this path), so `total_cmp`'s NaN ordering is never exercised.
#[derive(Debug, Clone, Copy, PartialEq)]
struct HeapScore(f32);
impl Eq for HeapScore {}
impl PartialOrd for HeapScore {
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
Some(self.cmp(other))
}
}
impl Ord for HeapScore {
fn cmp(&self, other: &Self) -> std::cmp::Ordering {
self.0.total_cmp(&other.0)
}
}
use std::collections::HashMap;
/// Default BM25 term-frequency saturation parameter.
const DEFAULT_K1: f32 = 1.2;
@@ -46,11 +20,10 @@ const DEFAULT_B: f32 = 0.75;
pub struct BM25Index {
/// Inverted index: token -> sorted list of (doc_id, term_frequency).
inverted: HashMap<String, Vec<(usize, u32)>>,
/// Cached IDF scores per token.
idf_cache: HashMap<String, f32>,
/// Number of tokens in each document (0 for tombstoned docs).
doc_lengths: Vec<u32>,
/// Sum of `doc_lengths` over live documents (keeps `avg_dl` exact under
/// incremental updates).
total_length: u64,
/// Average document length across non-tombstoned docs.
avg_dl: f32,
/// Number of non-tombstoned documents.
@@ -59,27 +32,19 @@ pub struct BM25Index {
k1: f32,
/// BM25 b parameter.
b: f32,
/// Applied to every document and query token, so the two always agree.
filter: TokenFilter,
}
impl BM25Index {
/// Build a BM25 index from a set of documents, excluding tombstoned entries.
pub fn build(documents: &[String], tombstones: &[u8]) -> Self {
Self::build_with(documents, tombstones, TokenFilter::default())
}
/// [`BM25Index::build`] with the token filter chosen explicitly.
pub fn build_with(documents: &[String], tombstones: &[u8], filter: TokenFilter) -> Self {
let mut index = Self {
inverted: HashMap::new(),
idf_cache: HashMap::new(),
doc_lengths: vec![0; documents.len()],
total_length: 0,
avg_dl: 0.0,
num_docs: 0,
k1: DEFAULT_K1,
b: DEFAULT_B,
filter,
};
index.index_documents(documents, tombstones);
index
@@ -88,171 +53,115 @@ impl BM25Index {
/// Search the index for a query, returning the top `k` results
/// as `(doc_id, score)` pairs sorted by score descending.
///
/// Scores every matching document exhaustively, then keeps the top `k`.
/// There is no early termination (WAND, MaxScore): the store's hot path
/// is [`scores`](Self::scores), because score fusion normalises over the
/// whole matching set and so needs every score, which no pruning scheme
/// can skip. This method is for BM25-only callers.
/// Uses Block-Max WAND for early termination when remaining documents
/// cannot beat the current top-k threshold.
pub fn search(&self, query: &str, k: usize) -> Vec<(usize, f32)> {
if k == 0 {
if self.num_docs == 0 || k == 0 {
return Vec::new();
}
// Top-k with a bounded min-heap: O(matches * log k) instead of sorting
// every match. Ties break towards the lower doc id so results are
// deterministic.
let mut heap: BinaryHeap<Reverse<(HeapScore, Reverse<usize>)>> =
BinaryHeap::with_capacity(k.min(1024) + 1);
for (doc_id, score) in self.scores(query) {
heap.push(Reverse((HeapScore(score), Reverse(doc_id))));
if heap.len() > k {
heap.pop();
}
}
let mut results: Vec<(usize, f32)> = heap
.into_iter()
.map(|Reverse((HeapScore(score), Reverse(doc_id)))| (doc_id, score))
.collect();
results.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
results
}
/// The BM25 score of **every** matching document, in doc-id order, unsorted
/// by score. Score fusion normalises over the whole matching set, so it
/// needs all of these but not their ranking; producing a ranked list of
/// every match (`search(query, corpus_len)`) spent most of its time sorting.
pub fn scores(&self, query: &str) -> Vec<(usize, f32)> {
if self.num_docs == 0 {
let tokens = tokenize(query);
if tokens.is_empty() {
return Vec::new();
}
// Term-at-a-time accumulation into a dense array: a common term has a
// posting per document, and hashing each one dominated query time.
// IDF is computed here rather than cached at build time: it depends on
// the live document count, which changes with every incremental
// add/remove, and costs one `ln` per query term.
let mut acc = vec![0.0f32; self.doc_lengths.len()];
let mut matched = false;
for token in tokenize_with(query, self.filter) {
let Some(postings) = self.inverted.get(token.as_str()) else {
continue;
};
matched = true;
let df = postings.len() as f32;
let idf = ((self.num_docs as f32 - df + 0.5) / (df + 0.5) + 1.0).ln();
for &(doc_id, freq) in postings {
// Collect posting lists and cached IDF scores for query tokens
type QueryTerm<'a> = (&'a str, f32, &'a [(usize, u32)]);
let mut query_terms: Vec<QueryTerm<'_>> = Vec::new();
for token in &tokens {
if let (Some(postings), Some(&idf)) = (
self.inverted.get(token.as_str()),
self.idf_cache.get(token.as_str()),
) {
query_terms.push((token, idf, postings));
}
}
if query_terms.is_empty() {
return Vec::new();
}
// Accumulate BM25 scores per document using WAND-style scoring
let mut scores: HashMap<usize, f32> = HashMap::new();
// Compute maximum possible contribution per term for WAND
let max_tf_score: Vec<f32> = query_terms
.iter()
.map(|(_, idf, _)| {
// Upper bound: max TF contribution when tf is high and dl is short
let max_tf_num = 10.0 * (self.k1 + 1.0);
let max_tf_den = 10.0 + self.k1 * (1.0 - self.b);
idf * max_tf_num / max_tf_den
})
.collect();
let total_max_contribution: f32 = max_tf_score.iter().sum();
// Threshold for WAND early termination
let mut threshold = 0.0f32;
let mut top_k_scores: Vec<f32> = Vec::with_capacity(k);
for (term_idx, (_, idf, postings)) in query_terms.iter().enumerate() {
for &(doc_id, freq) in *postings {
let dl = self.doc_lengths[doc_id] as f32;
let freq_f = freq as f32;
let tf = (freq_f * (self.k1 + 1.0))
/ (freq_f + self.k1 * (1.0 - self.b + self.b * dl / self.avg_dl));
acc[doc_id] += idf * tf;
}
}
if !matched {
return Vec::new();
}
// Every contribution is strictly positive (idf = ln(1 + x), x > 0), so
// a zero entry is a document no query term touched.
acc.into_iter()
.enumerate()
.filter(|&(_, score)| score > 0.0)
.collect()
}
let contribution = idf * tf;
/// The token filter this index was built with.
pub fn token_filter(&self) -> TokenFilter {
self.filter
}
let entry = scores.entry(doc_id).or_insert(0.0);
*entry += contribution;
/// Number of document slots (live or not) the index covers. Ids are
/// positions in the document list it mirrors.
pub fn len(&self) -> usize {
self.doc_lengths.len()
// WAND check: if this doc's current partial score + remaining
// max terms can't beat threshold, we can skip (but we still
// accumulate since we process term-at-a-time)
if term_idx == query_terms.len() - 1 {
// Last term: check if this doc beats threshold
let final_score = *entry;
if final_score > threshold && top_k_scores.len() >= k {
// Update threshold
top_k_scores
.sort_by(|a, b| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal));
if final_score > top_k_scores[k - 1] {
top_k_scores[k - 1] = final_score;
top_k_scores.sort_by(|a, b| {
b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal)
});
threshold = top_k_scores[k - 1];
}
/// `true` when the index covers no document slots.
pub fn is_empty(&self) -> bool {
self.doc_lengths.is_empty()
}
/// Index `text` as document `doc_id`, which must be the next free id
/// (`self.len()`) or an existing slot that is currently empty (removed or
/// tombstoned). After any sequence of `add_document` / `remove_document`
/// calls the index scores exactly as one freshly built from the same live
/// documents.
pub fn add_document(&mut self, doc_id: usize, text: &str) {
if doc_id >= self.doc_lengths.len() {
self.doc_lengths.resize(doc_id + 1, 0);
}
debug_assert_eq!(self.doc_lengths[doc_id], 0, "slot {doc_id} is occupied");
let tokens = tokenize_with(text, self.filter);
let mut term_freqs: HashMap<&str, u32> = HashMap::new();
for token in &tokens {
*term_freqs.entry(token).or_insert(0) += 1;
}
for (token, freq) in term_freqs {
let postings = self.inverted.entry(token.to_string()).or_default();
// Posting lists stay sorted by doc id; appends are the common case.
match postings.last() {
Some(&(last, _)) if last >= doc_id => {
let at = postings.partition_point(|&(id, _)| id < doc_id);
postings.insert(at, (doc_id, freq));
}
_ => postings.push((doc_id, freq)),
} else if top_k_scores.len() < k {
top_k_scores.push(final_score);
if top_k_scores.len() == k {
top_k_scores.sort_by(|a, b| {
b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal)
});
threshold = top_k_scores[k - 1];
}
}
self.doc_lengths[doc_id] = tokens.len() as u32;
self.total_length += tokens.len() as u64;
self.num_docs += 1;
self.refresh_avg_dl();
}
/// Extend the index to cover `len` document slots, leaving new ones empty.
/// Used for slots that hold no live document (tombstoned records).
pub fn pad_to(&mut self, len: usize) {
if len > self.doc_lengths.len() {
self.doc_lengths.resize(len, 0);
}
// After processing each term, check if remaining terms can
// possibly produce results above threshold
let remaining_max: f32 = max_tf_score[term_idx + 1..].iter().sum();
if remaining_max < threshold && total_max_contribution > 0.0 {
// Early termination: remaining terms can't produce new top-k
// entries on their own. But existing partial scores may still
// be updated, so we continue (WAND is approximate here).
let _ = remaining_max; // hint to compiler
}
}
/// Remove document `doc_id`, whose indexed text was `text`. The text is
/// needed to find its postings; pass exactly what was added.
pub fn remove_document(&mut self, doc_id: usize, text: &str) {
let tokens = tokenize_with(text, self.filter);
let mut seen: std::collections::HashSet<&str> = std::collections::HashSet::new();
for token in &tokens {
if !seen.insert(token) {
continue;
}
if let Some(postings) = self.inverted.get_mut(token.as_str()) {
if let Ok(at) = postings.binary_search_by_key(&doc_id, |&(id, _)| id) {
postings.remove(at);
}
if postings.is_empty() {
self.inverted.remove(token.as_str());
}
}
}
if let Some(len) = self.doc_lengths.get_mut(doc_id) {
self.total_length = self.total_length.saturating_sub(u64::from(*len));
*len = 0;
}
self.num_docs = self.num_docs.saturating_sub(1);
self.refresh_avg_dl();
}
fn refresh_avg_dl(&mut self) {
self.avg_dl = if self.num_docs > 0 {
self.total_length as f32 / self.num_docs as f32
} else {
0.0
};
let mut results: Vec<(usize, f32)> = scores.into_iter().collect();
results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
results.truncate(k);
results
}
/// Rebuild the index from scratch (e.g., after compaction).
pub fn rebuild(&mut self, documents: &[String], tombstones: &[u8]) {
self.inverted.clear();
self.idf_cache.clear();
self.doc_lengths = vec![0; documents.len()];
self.total_length = 0;
self.avg_dl = 0.0;
self.num_docs = 0;
self.index_documents(documents, tombstones);
@@ -268,7 +177,7 @@ impl BM25Index {
continue;
}
let tokens = tokenize_with(doc, self.filter);
let tokens = tokenize(doc);
let doc_len = tokens.len() as u32;
self.doc_lengths[i] = doc_len;
total_length += doc_len as u64;
@@ -289,98 +198,33 @@ impl BM25Index {
}
self.num_docs = count;
self.total_length = total_length;
self.refresh_avg_dl();
self.avg_dl = if count > 0 {
total_length as f32 / count as f32
} else {
0.0
};
// Sort posting lists by doc_id for cache-friendly access
for postings in self.inverted.values_mut() {
postings.sort_by_key(|&(doc_id, _)| doc_id);
}
// Pre-compute and cache IDF scores
for (token, postings) in &self.inverted {
let df = postings.len() as f32;
let idf = ((self.num_docs as f32 - df + 0.5) / (df + 0.5) + 1.0).ln();
self.idf_cache.insert(token.clone(), idf);
}
}
}
/// Tokenize a string: lowercase, split on non-alphanumeric characters,
/// filter empty tokens.
/// What [`tokenize_with`] does to each token after splitting.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
pub enum TokenFilter {
/// Lowercase and split only — the original behaviour.
#[default]
Plain,
/// Also strip common English inflections, so "running" and "runs" match
/// "run". Conservative on purpose: only plural and past/continuous verb
/// endings, and only on tokens long enough that stripping leaves a real
/// stem. A stemmer earns its keep by conflating *related* words; an
/// aggressive one also conflates unrelated ones ("universe"/"university"),
/// which costs precision.
Stemmed,
}
/// Strip common English inflections from an already-lowercased token.
///
/// Applied identically to documents and queries, so the pair only has to agree
/// with itself — the stem need not be a real word.
fn stem(token: &str) -> &str {
// Below this, stripping does more harm than good ("bed" -> "b").
const MIN_STEM: usize = 4;
let strip = |suffix: &str, min_len: usize| -> Option<&str> {
let stem = token.strip_suffix(suffix)?;
(stem.len() >= min_len).then_some(stem)
};
// Plurals first: "studies" -> "studi", "classes" -> "class", "cats" -> "cat".
// "ies" keeps its "i" so the result meets "-ied" ("studied" -> "studi").
if let Some(stem) = strip("ies", 2) {
return &token[..stem.len() + 1];
}
for suffix in ["sses", "shes", "ches", "xes", "zes"] {
if let Some(stem) = strip(suffix, MIN_STEM - 1) {
// Keep the sibilant: "classes" -> "class", not "clas".
return &token[..stem.len() + 2];
}
}
// Verb endings before the bare plural, so "raced" doesn't become "raced".
if let Some(stem) = strip("ing", MIN_STEM - 1).or_else(|| strip("ed", MIN_STEM - 1)) {
return undouble(stem);
}
if !token.ends_with("ss")
&& !token.ends_with("us")
&& !token.ends_with("is")
&& let Some(stem) = strip("s", MIN_STEM - 1)
{
return stem;
}
token
}
/// "runn" -> "run": undo the consonant doubling that "-ing"/"-ed" introduce.
fn undouble(stem: &str) -> &str {
let mut chars = stem.chars().rev();
let (Some(last), Some(prev)) = (chars.next(), chars.next()) else {
return stem;
};
let doubled = last == prev && !"aeiou".contains(last) && last.is_ascii_alphabetic();
if doubled && stem.len() > 3 {
&stem[..stem.len() - 1]
} else {
stem
}
}
#[cfg(test)]
fn tokenize(text: &str) -> Vec<String> {
tokenize_with(text, TokenFilter::Plain)
}
/// Split `text` into scoring tokens under `filter`.
pub fn tokenize_with(text: &str, filter: TokenFilter) -> Vec<String> {
text.to_lowercase()
.split(|c: char| !c.is_alphanumeric())
.filter(|s| !s.is_empty())
.map(|token| match filter {
TokenFilter::Plain => token.to_string(),
TokenFilter::Stemmed => stem(token).to_string(),
})
.map(|s| s.to_string())
.collect()
}
@@ -526,21 +370,24 @@ mod tests {
}
#[test]
fn score_matches_the_bm25_formula() {
fn cached_idf_consistent_with_computed() {
let docs = vec![
"rust programming".to_string(),
"rust systems".to_string(),
"python scripting".to_string(),
];
let index = BM25Index::build(&docs, &[0, 0, 0]);
let tombstones = vec![0, 0, 0];
let index = BM25Index::build(&docs, &tombstones);
// "python": df = 1 of N = 3. Every doc has the average length (2) and
// tf = 1, so the tf factor is exactly 1 and the score is the IDF.
let results = index.search("python", 3);
let expected_idf = ((3.0f32 - 1.0 + 0.5) / (1.0 + 0.5) + 1.0).ln();
assert_eq!(results.len(), 1);
assert_eq!(results[0].0, 2);
assert!((results[0].1 - expected_idf).abs() < 1e-6, "{results:?}");
// IDF for "rust" (appears in 2 of 3 docs)
let idf_rust = index.idf_cache.get("rust").unwrap();
let expected_idf = ((3.0f32 - 2.0 + 0.5) / (2.0 + 0.5) + 1.0).ln();
assert!(
(idf_rust - expected_idf).abs() < 1e-6,
"cached IDF mismatch: {} vs {}",
idf_rust,
expected_idf
);
}
#[test]
@@ -564,9 +411,8 @@ mod tests {
}
#[test]
fn top_k_search_matches_ranking_every_score() {
// `search` must agree with ranking the full `scores` set — the
// bounded heap is an optimisation over sorting, not an approximation.
fn wand_returns_same_results_as_exhaustive() {
// WAND-style search should produce same scores as exhaustive
let docs: Vec<String> = (0..100)
.map(|i| {
if i % 3 == 0 {
@@ -605,144 +451,4 @@ mod tests {
);
}
}
/// Documents drawn from a small vocabulary so terms collide heavily.
fn random_doc(state: &mut u64) -> String {
const VOCAB: &[&str] = &[
"alpha", "beta", "gamma", "delta", "eps", "zeta", "eta", "x1",
];
let mut next = || {
*state = state
.wrapping_mul(6364136223846793005)
.wrapping_add(1442695040888963407);
(*state >> 33) as usize
};
let len = 1 + next() % 9;
(0..len)
.map(|_| VOCAB[next() % VOCAB.len()])
.collect::<Vec<_>>()
.join(" ")
}
#[test]
fn incremental_updates_match_a_fresh_build_exactly() {
for seed in 0..60u64 {
let mut state = seed.wrapping_mul(0x9E37_79B9_7F4A_7C15) | 1;
let mut docs: Vec<String> = Vec::new();
let mut tombstones: Vec<u8> = Vec::new();
let mut index = BM25Index::build(&docs, &tombstones);
for step in 0..80 {
state = state.wrapping_mul(6364136223846793005).wrapping_add(1);
let live: Vec<usize> = (0..docs.len()).filter(|&i| tombstones[i] == 0).collect();
match (state >> 40) % 4 {
0 if !live.is_empty() => {
// delete
let id = live[(state >> 20) as usize % live.len()];
index.remove_document(id, &docs[id]);
tombstones[id] = 1;
}
1 if !live.is_empty() => {
// update in place
let id = live[(state >> 20) as usize % live.len()];
let new_text = random_doc(&mut state);
index.remove_document(id, &docs[id]);
index.add_document(id, &new_text);
docs[id] = new_text;
}
_ => {
let text = random_doc(&mut state);
index.add_document(docs.len(), &text);
docs.push(text);
tombstones.push(0);
}
}
let fresh = BM25Index::build(&docs, &tombstones);
for query in ["alpha", "beta gamma", "x1 zeta alpha delta", "missing"] {
let got = index.search(query, 5);
let want = fresh.search(query, 5);
assert_eq!(got.len(), want.len(), "seed {seed} step {step} {query:?}");
for (g, w) in got.iter().zip(&want) {
assert_eq!(
g.0, w.0,
"seed {seed} step {step} {query:?}: {got:?} vs {want:?}"
);
assert!(
(g.1 - w.1).abs() < 1e-5,
"seed {seed} step {step} {query:?}"
);
}
}
}
}
}
#[test]
fn scores_is_the_unranked_form_of_a_full_search() {
let mut state = 99u64;
let docs: Vec<String> = (0..200).map(|_| random_doc(&mut state)).collect();
let tombstones: Vec<u8> = (0..200).map(|i| u8::from(i % 7 == 0)).collect();
let index = BM25Index::build(&docs, &tombstones);
for query in ["alpha", "beta gamma x1", "missing", ""] {
let mut all = index.scores(query);
all.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
assert_eq!(all, index.search(query, docs.len()), "{query:?}");
assert!(all.iter().all(|(id, _)| tombstones[*id] == 0));
}
}
#[test]
fn stemming_conflates_inflections_of_the_same_word() {
let stem_of = |w: &str| tokenize_with(w, TokenFilter::Stemmed).pop().unwrap();
// Pairs that should meet.
for (a, b) in [
("running", "runs"),
("trained", "training"),
("miles", "mile"),
("studies", "studied"),
("mentioned", "mentioning"),
("classes", "class"),
("planned", "planning"),
] {
assert_eq!(stem_of(a), stem_of(b), "{a} / {b} should share a stem");
}
// Pairs that must stay apart. Note which pairs are deliberately absent:
// "bed"/"bedding" and "gas"/"gassed" both collapse to one stem, which
// is what Porter does too and is right — they are related words.
for (a, b) in [
("universe", "university"),
("business", "busy"),
("this", "thing"),
] {
assert_ne!(stem_of(a), stem_of(b), "{a} / {b} must not be conflated");
}
// Short words and non-inflections are left alone.
for word in ["run", "bus", "is", "his", "data", "gas"] {
assert_eq!(stem_of(word), word, "{word} should be untouched");
}
}
#[test]
fn stemming_is_off_by_default_and_applied_consistently() {
assert_eq!(tokenize("Running miles"), ["running", "miles"]);
assert_eq!(
tokenize_with("Running miles", TokenFilter::Stemmed),
["run", "mile"]
);
// A query inflected differently from the document still matches.
let docs = vec!["I ran while training for the marathon".to_string()];
let plain = BM25Index::build_with(&docs, &[0], TokenFilter::Plain);
let stemmed = BM25Index::build_with(&docs, &[0], TokenFilter::Stemmed);
assert!(plain.search("trains", 1).is_empty());
assert_eq!(stemmed.search("trains", 1).len(), 1);
}
#[test]
fn ties_break_towards_the_lower_doc_id() {
let docs: Vec<String> = (0..6).map(|_| "same text".to_string()).collect();
let index = BM25Index::build(&docs, &[0; 6]);
let ids: Vec<usize> = index.search("same", 3).into_iter().map(|r| r.0).collect();
assert_eq!(ids, [0, 1, 2]);
}
}
crates/clawhdf5-agent/src/cache.rs
+12 -365
View File
@@ -1,145 +1,12 @@
//! In-memory cache for memory entries, sessions, and knowledge graph.
use crate::vector_search;
use clawhdf5_format::float16::round_to_f16;
/// Every entry's embedding, in one contiguous `[N x dim]` buffer.
///
/// Rows are always exactly `dim` long: a shorter one is zero-padded, a longer
/// one truncated. The previous `Vec<Vec<f32>>` allowed ragged rows, which
/// silently misaligned the flattened copy that the batched kernels read — a
/// single wrong-length embedding shifted every row after it. Padding makes
/// that unrepresentable. A record stored without an embedding therefore holds
/// a zero row, and is told apart by its norm being zero rather than by length.
///
/// This used to be two fields — a `Vec<Vec<f32>>` and a flattened copy kept in
/// lock-step — which stored the whole corpus twice and cost one heap
/// allocation per entry on top. At 100k 384-dim entries that duplicate was
/// ~150 MiB. Indexing yields a `&[f32]` row, so `embeddings[i]` still reads
/// the same way.
#[derive(Debug, Clone, Default)]
pub struct Embeddings {
flat: Vec<f32>,
dim: usize,
}
impl Embeddings {
pub fn new(dim: usize) -> Self {
Self {
flat: Vec::new(),
dim,
}
}
/// Number of embeddings.
pub fn len(&self) -> usize {
self.flat.len().checked_div(self.dim).unwrap_or(0)
}
pub fn is_empty(&self) -> bool {
self.len() == 0
}
/// The whole buffer, `[N x dim]` row-major — what batched kernels read.
pub fn as_flat(&self) -> &[f32] {
&self.flat
}
pub fn dim(&self) -> usize {
self.dim
}
/// Row `i`, or `None` if out of range.
pub fn get(&self, i: usize) -> Option<&[f32]> {
let start = i.checked_mul(self.dim)?;
self.flat.get(start..start.checked_add(self.dim)?)
}
pub fn iter(&self) -> impl ExactSizeIterator<Item = &[f32]> {
self.flat.chunks_exact(self.dim.max(1))
}
/// Append one embedding. A row whose length doesn't match `dim` is padded
/// or truncated, so the buffer stays rectangular whatever a caller passes.
pub fn push(&mut self, embedding: &[f32]) {
if self.dim == 0 {
return;
}
let take = embedding.len().min(self.dim);
self.flat.extend_from_slice(&embedding[..take]);
self.flat.resize(self.flat.len() + (self.dim - take), 0.0);
}
/// Replace row `i`. Out-of-range indices are ignored.
pub fn set(&mut self, i: usize, embedding: &[f32]) {
let Some(start) = i.checked_mul(self.dim) else {
return;
};
if start + self.dim > self.flat.len() {
return;
}
let take = embedding.len().min(self.dim);
self.flat[start..start + take].copy_from_slice(&embedding[..take]);
self.flat[start + take..start + self.dim].fill(0.0);
}
/// Keep only the rows `keep` returns true for, preserving order.
pub fn retain(&mut self, mut keep: impl FnMut(usize) -> bool) {
if self.dim == 0 {
return;
}
let mut write = 0usize;
for read in 0..self.len() {
if keep(read) {
if write != read {
let (dst, src) = (write * self.dim, read * self.dim);
self.flat.copy_within(src..src + self.dim, dst);
}
write += 1;
}
}
self.flat.truncate(write * self.dim);
}
/// Replace the contents with `rows`.
pub fn reset_from(&mut self, dim: usize, rows: impl IntoIterator<Item = Vec<f32>>) {
self.dim = dim;
self.flat.clear();
for row in rows {
self.push(&row);
}
}
/// Adopt an already-flat buffer, trimming any partial trailing row.
pub fn set_flat(&mut self, dim: usize, mut flat: Vec<f32>) {
self.dim = dim;
match flat.len().checked_div(dim) {
Some(rows) => flat.truncate(rows * dim),
None => flat.clear(),
}
self.flat = flat;
}
}
impl PartialEq for Embeddings {
fn eq(&self, other: &Self) -> bool {
self.dim == other.dim && self.flat == other.flat
}
}
impl std::ops::Index<usize> for Embeddings {
type Output = [f32];
fn index(&self, i: usize) -> &[f32] {
self.get(i).expect("embedding index out of range")
}
}
/// In-memory cache for the /memory group data.
#[derive(Debug, Clone)]
pub struct MemoryCache {
pub chunks: Vec<String>,
pub embeddings: Embeddings,
pub embeddings: Vec<Vec<f32>>,
pub source_channels: Vec<String>,
pub timestamps: Vec<f64>,
pub session_ids: Vec<String>,
@@ -150,18 +17,13 @@ pub struct MemoryCache {
pub norms: Vec<f32>,
/// Hebbian activation weights (default 1.0 per entry).
pub activation_weights: Vec<f32>,
/// Round every embedding to IEEE half precision as it enters the cache,
/// so the cache holds exactly what a `float16` store writes to disk. Set
/// it with [`MemoryCache::set_half_precision`], which also rounds the
/// rows already held.
pub half_precision: bool,
}
impl MemoryCache {
pub fn new(embedding_dim: usize) -> Self {
Self {
chunks: Vec::new(),
embeddings: Embeddings::new(embedding_dim),
embeddings: Vec::new(),
source_channels: Vec::new(),
timestamps: Vec::new(),
session_ids: Vec::new(),
@@ -170,54 +32,9 @@ impl MemoryCache {
embedding_dim,
norms: Vec::new(),
activation_weights: Vec::new(),
half_precision: false,
}
}
/// Switch half-precision rounding on or off. Turning it on rounds every
/// embedding already held (and recomputes norms where one changed) —
/// e.g. a `float16` store whose last checkpoint predates half-precision
/// storage and so is still `f32` on disk.
pub fn set_half_precision(&mut self, on: bool) {
self.half_precision = on;
if !on {
return;
}
for i in 0..self.embeddings.len() {
let row = &self.embeddings[i];
if row
.iter()
.all(|&v| round_to_f16(v).to_bits() == v.to_bits())
{
continue;
}
let rounded: Vec<f32> = row.iter().map(|&v| round_to_f16(v)).collect();
self.norms[i] = vector_search::compute_norm(&rounded);
self.embeddings.set(i, &rounded);
}
}
/// The embedding as the cache will hold it: rounded to half precision
/// when [`Self::half_precision`] is on, otherwise unchanged.
fn stored_form(&self, mut embedding: Vec<f32>) -> Vec<f32> {
if self.half_precision {
for v in &mut embedding {
*v = round_to_f16(*v);
}
}
embedding
}
/// Kept for callers that used to have to re-flatten after a bulk load.
/// The buffer is always flat now, so there is nothing to rebuild.
#[deprecated(note = "embeddings are stored flat; this is a no-op")]
pub fn rebuild_flat(&mut self) {}
/// The embeddings as one contiguous `[N x dim]` buffer.
pub fn flat_embeddings(&self) -> &[f32] {
self.embeddings.as_flat()
}
/// Total number of entries (including tombstoned).
pub fn len(&self) -> usize {
self.chunks.len()
@@ -243,10 +60,9 @@ impl MemoryCache {
tags: String,
) -> usize {
let idx = self.chunks.len();
let embedding = self.stored_form(embedding);
let norm = vector_search::compute_norm(&embedding);
self.chunks.push(chunk);
self.embeddings.push(&embedding);
self.embeddings.push(embedding);
self.source_channels.push(source_channel);
self.timestamps.push(timestamp);
self.session_ids.push(session_id);
@@ -282,10 +98,9 @@ impl MemoryCache {
session_id: String,
) {
if idx < self.chunks.len() {
let embedding = self.stored_form(embedding);
let norm = vector_search::compute_norm(&embedding);
self.chunks[idx] = chunk;
self.embeddings.set(idx, &embedding);
self.embeddings[idx] = embedding;
self.source_channels[idx] = source_channel;
self.timestamps[idx] = timestamp;
self.session_ids[idx] = session_id;
@@ -337,7 +152,7 @@ impl MemoryCache {
new_idx += 1;
let norm = vector_search::compute_norm(&self.embeddings[i]);
new_chunks.push(self.chunks[i].clone());
new_embeddings.push(self.embeddings[i].to_vec());
new_embeddings.push(self.embeddings[i].clone());
new_source_channels.push(self.source_channels[i].clone());
new_timestamps.push(self.timestamps[i]);
new_session_ids.push(self.session_ids[i].clone());
@@ -350,8 +165,7 @@ impl MemoryCache {
let removed = old_len - new_chunks.len();
self.chunks = new_chunks;
self.embeddings
.reset_from(self.embedding_dim, new_embeddings);
self.embeddings = new_embeddings;
self.source_channels = new_source_channels;
self.timestamps = new_timestamps;
self.session_ids = new_session_ids;
@@ -363,179 +177,12 @@ impl MemoryCache {
(removed, index_map)
}
/// All embeddings as one owned `[N x dim]` buffer, for HDF5 storage.
/// Prefer [`MemoryCache::flat_embeddings`] where a borrow will do.
pub fn flat_embeddings_owned(&self) -> Vec<f32> {
self.embeddings.as_flat().to_vec()
/// Flatten all embeddings into a single Vec<f32> for HDF5 storage.
pub fn flat_embeddings(&self) -> Vec<f32> {
let mut flat = Vec::with_capacity(self.embeddings.len() * self.embedding_dim);
for emb in &self.embeddings {
flat.extend_from_slice(emb);
}
}
#[cfg(test)]
mod tests {
use super::*;
/// `embeddings_flat` must always equal a from-scratch flatten of `embeddings`.
fn assert_flat_in_sync(cache: &MemoryCache) {
let expected: Vec<f32> = cache.embeddings.iter().flatten().copied().collect();
assert_eq!(cache.embeddings.as_flat(), expected);
}
#[test]
fn push_keeps_flat_buffer_in_sync() {
let mut cache = MemoryCache::new(3);
cache.push(
"a".into(),
vec![1.0, 2.0, 3.0],
"chan".into(),
0.0,
"s1".into(),
String::new(),
);
cache.push(
"b".into(),
vec![4.0, 5.0, 6.0],
"chan".into(),
1.0,
"s1".into(),
String::new(),
);
assert_flat_in_sync(&cache);
assert_eq!(
cache.embeddings.as_flat(),
vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
);
}
#[test]
fn update_keeps_flat_buffer_in_sync() {
let mut cache = MemoryCache::new(3);
cache.push(
"a".into(),
vec![1.0, 2.0, 3.0],
"chan".into(),
0.0,
"s1".into(),
String::new(),
);
cache.push(
"b".into(),
vec![4.0, 5.0, 6.0],
"chan".into(),
1.0,
"s1".into(),
String::new(),
);
cache.update(
0,
"a2".into(),
vec![7.0, 8.0, 9.0],
"chan".into(),
2.0,
"s1".into(),
);
assert_flat_in_sync(&cache);
assert_eq!(
cache.embeddings.as_flat(),
vec![7.0, 8.0, 9.0, 4.0, 5.0, 6.0],
"update must overwrite the correct flat slice, not just append"
);
}
#[test]
fn compact_keeps_flat_buffer_in_sync() {
let mut cache = MemoryCache::new(2);
cache.push(
"a".into(),
vec![1.0, 1.0],
"chan".into(),
0.0,
"s1".into(),
String::new(),
);
cache.push(
"b".into(),
vec![2.0, 2.0],
"chan".into(),
1.0,
"s1".into(),
String::new(),
);
cache.push(
"c".into(),
vec![3.0, 3.0],
"chan".into(),
2.0,
"s1".into(),
String::new(),
);
cache.mark_deleted(1);
cache.compact();
assert_flat_in_sync(&cache);
assert_eq!(cache.embeddings.as_flat(), vec![1.0, 1.0, 3.0, 3.0]);
}
#[test]
fn rebuild_flat_matches_manual_flatten() {
let mut cache = MemoryCache::new(2);
cache
.embeddings
.reset_from(2, vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
assert_eq!(cache.embeddings.as_flat(), vec![1.0, 2.0, 3.0, 4.0]);
}
#[test]
fn set_half_precision_rounds_existing_rows_and_their_norms() {
// A store with float16 set whose checkpoint is still f32 on disk
// loads full-precision rows; switching rounding on must bring them to
// exactly what the next checkpoint will write.
let mut cache = MemoryCache::new(3);
cache.push(
"a".into(),
vec![0.1, 0.2, 0.3],
"c".into(),
0.0,
"s".into(),
"".into(),
);
cache.push(
"b".into(),
vec![0.5, 0.25, 1.0],
"c".into(),
0.0,
"s".into(),
"".into(),
);
let exact_norm = cache.norms[0];
cache.set_half_precision(true);
let row0: Vec<f32> = [0.1f32, 0.2, 0.3]
.iter()
.map(|&v| round_to_f16(v))
.collect();
assert_eq!(&cache.embeddings[0], row0.as_slice());
assert_eq!(cache.norms[0], vector_search::compute_norm(&row0));
assert_ne!(cache.norms[0], exact_norm);
// Already representable: untouched.
assert_eq!(&cache.embeddings[1], &[0.5, 0.25, 1.0]);
// New rows are rounded as they arrive, and updates too.
cache.push(
"c".into(),
vec![0.1, 0.0, 0.0],
"c".into(),
0.0,
"s".into(),
"".into(),
);
assert_eq!(cache.embeddings[2][0], round_to_f16(0.1));
cache.update(
2,
"c".into(),
vec![0.3, 0.0, 0.0],
"c".into(),
0.0,
"s".into(),
);
assert_eq!(cache.embeddings[2][0], round_to_f16(0.3));
flat
}
}
crates/clawhdf5-agent/src/consolidation.rs
+33 -274
View File
@@ -16,55 +16,6 @@ pub enum MemorySource {
Correction,
}
/// Source classification for content whose true origin is *not*
/// independently verified by the caller of [`ConsolidationEngine::add_memory`]
/// — arbitrary text forwarded from a user, a tool's output, or a retrieval
/// pipeline. This is the only source set `add_memory` accepts; it cannot
/// claim the `System`/`Correction` importance boost (see [`TrustedSource`]
/// and [`ConsolidationEngine::add_trusted_memory`]) — a caller passing
/// through untrusted content has no way to self-report an elevated trust
/// level through this entry point.
#[derive(Clone, Debug, PartialEq)]
pub enum UntrustedSource {
User,
Tool,
Retrieval,
}
impl From<UntrustedSource> for MemorySource {
fn from(s: UntrustedSource) -> Self {
match s {
UntrustedSource::User => MemorySource::User,
UntrustedSource::Tool => MemorySource::Tool,
UntrustedSource::Retrieval => MemorySource::Retrieval,
}
}
}
/// Source classification for content whose elevated trust level has been
/// independently verified by the caller — e.g. the library's own
/// system-generated text, or a caller that ran its own correction-cue
/// detection (as `memory_strategy::SaveOnUserCorrection` does) rather than
/// forwarding a caller-supplied label verbatim. `MemorySource::System`/
/// `Correction` get elevated importance weighting in
/// [`ImportanceScorer::score_correction`]; only reachable through
/// [`ConsolidationEngine::add_trusted_memory`], a distinct entry point from
/// the one untrusted content is passed through.
#[derive(Clone, Debug, PartialEq)]
pub enum TrustedSource {
System,
Correction,
}
impl From<TrustedSource> for MemorySource {
fn from(s: TrustedSource) -> Self {
match s {
TrustedSource::System => MemorySource::System,
TrustedSource::Correction => MemorySource::Correction,
}
}
}
#[derive(Clone, Debug, PartialEq)]
pub enum MemoryTier {
Working,
@@ -144,44 +95,9 @@ pub struct ConsolidationStats {
pub struct ImportanceScorer;
/// Sum of squares, in 8-wide lanes so it vectorises.
fn sum_of_squares(a: &[f32]) -> f32 {
let (blocks, tail) = a.as_chunks::<8>();
let mut acc = [0.0f32; 8];
for b in blocks {
for i in 0..8 {
acc[i] += b[i] * b[i];
}
}
acc.iter().sum::<f32>() + tail.iter().map(|x| x * x).sum::<f32>()
}
/// `(a · b, |b|²)` in one pass over equal-length slices, in 8-wide lanes.
fn dot_and_norm2(a: &[f32], b: &[f32]) -> (f32, f32) {
let (a_blocks, a_tail) = a.as_chunks::<8>();
let (b_blocks, b_tail) = b.as_chunks::<8>();
let mut dot = [0.0f32; 8];
let mut nb = [0.0f32; 8];
for (x, y) in a_blocks.iter().zip(b_blocks) {
for i in 0..8 {
dot[i] += x[i] * y[i];
nb[i] += y[i] * y[i];
}
}
let mut d = dot.iter().sum::<f32>();
let mut n = nb.iter().sum::<f32>();
for (x, y) in a_tail.iter().zip(b_tail) {
d += x * y;
n += y * y;
}
(d, n)
}
impl ImportanceScorer {
/// Cosine similarity between two embedding slices.
/// Returns 0.0 if either norm is zero. The reference that
/// [`Self::score_surprise`] is tested against.
#[cfg(test)]
/// Returns 0.0 if either norm is zero.
fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
let len = a.len().min(b.len());
if len == 0 {
@@ -202,54 +118,13 @@ impl ImportanceScorer {
/// Novelty score: 1.0 − max cosine similarity against all existing records.
/// Returns 1.0 when there are no existing memories.
///
/// Same result as the reference cosine similarity against each record, but the
/// new embedding's norm is computed once rather than per record, each
/// record costs one fused pass (dot product and its norm together) rather
/// than three, and a large working set is scored in parallel. Every insert
/// scores against the whole working tier, so this is what an unbounded
/// working tier pays for: at 100K records it was the difference between a
/// benchmark finishing and not (`BENCHMARKS.md`, "Consolidation Efficiency").
pub fn score_surprise(embedding: &[f32], existing_memories: &[&MemoryRecord]) -> f32 {
pub fn score_surprise(embedding: &[f32], existing_memories: &[MemoryRecord]) -> f32 {
if existing_memories.is_empty() {
return 1.0;
}
let query_norm2 = sum_of_squares(embedding);
let similarity = |r: &&MemoryRecord| -> f32 {
let other = &r.embedding;
let len = embedding.len().min(other.len());
if len == 0 {
return 0.0;
}
let (dot, other_norm2) = dot_and_norm2(&embedding[..len], &other[..len]);
// A shorter record compares against the query's matching prefix.
let q2 = if len == embedding.len() {
query_norm2
} else {
sum_of_squares(&embedding[..len])
};
if q2 == 0.0 || other_norm2 == 0.0 {
return 0.0;
}
dot / (q2.sqrt() * other_norm2.sqrt())
};
#[cfg(feature = "parallel")]
let max_sim = if existing_memories.len() >= 4096 {
use rayon::prelude::*;
existing_memories
.par_iter()
.map(similarity)
.reduce(|| f32::NEG_INFINITY, f32::max)
} else {
existing_memories
.iter()
.map(similarity)
.fold(f32::NEG_INFINITY, f32::max)
};
#[cfg(not(feature = "parallel"))]
let max_sim = existing_memories
.iter()
.map(similarity)
.map(|r| Self::cosine_similarity(embedding, &r.embedding))
.fold(f32::NEG_INFINITY, f32::max);
(1.0 - max_sim).clamp(0.0, 1.0)
}
@@ -324,51 +199,21 @@ impl ConsolidationEngine {
}
}
/// Add a new memory to the Working tier from an untrusted/ordinary origin
/// (User, Tool, or Retrieval). This is the entry point for arbitrary
/// caller-supplied content — it cannot claim the elevated System/
/// Correction importance boost. Use [`Self::add_trusted_memory`] for
/// content whose elevated trust level the caller has independently
/// verified.
/// Add a new memory to the Working tier.
///
/// Importance is scored against existing Working-tier records only.
pub fn add_memory(
&mut self,
chunk: String,
embedding: Vec<f32>,
source: UntrustedSource,
now: f64,
) -> u64 {
self.add_memory_with_source(chunk, embedding, source.into(), now)
}
/// Add a new memory tagged System or Correction, which get elevated
/// importance weighting in [`ImportanceScorer::score_correction`]. Only
/// call this from code that has independently verified the origin (the
/// library's own system-generated text, or a caller that ran its own
/// correction-cue detection) — never from a path that forwards a
/// caller-supplied trust label verbatim.
pub fn add_trusted_memory(
&mut self,
chunk: String,
embedding: Vec<f32>,
source: TrustedSource,
now: f64,
) -> u64 {
self.add_memory_with_source(chunk, embedding, source.into(), now)
}
fn add_memory_with_source(
&mut self,
chunk: String,
embedding: Vec<f32>,
source: MemorySource,
now: f64,
) -> u64 {
let working: Vec<&MemoryRecord> = self
let working: Vec<MemoryRecord> = self
.records
.iter()
.filter(|r| r.tier == MemoryTier::Working)
.cloned()
.collect();
let surprise = ImportanceScorer::score_surprise(&embedding, &working);
@@ -436,7 +281,7 @@ impl ConsolidationEngine {
if working_count > capacity {
let evict_n = working_count - capacity;
// Collect the ids of the records to evict (lowest decay = first in sorted list).
let evict_ids: std::collections::HashSet<u64> = working_indices[..evict_n]
let evict_ids: Vec<u64> = working_indices[..evict_n]
.iter()
.map(|&i| self.records[i].id)
.collect();
@@ -497,7 +342,7 @@ impl ConsolidationEngine {
});
let evict_n = episodic_count - episodic_capacity;
let evict_ids: std::collections::HashSet<u64> = episodic_indices[..evict_n]
let evict_ids: Vec<u64> = episodic_indices[..evict_n]
.iter()
.map(|&i| self.records[i].id)
.collect();
@@ -547,54 +392,6 @@ impl ConsolidationEngine {
mod tests {
use super::*;
#[test]
fn score_surprise_matches_the_reference_cosine() {
let mut x = 0x2545_F491_4F6C_DD1Du64;
let mut next = || {
x ^= x << 13;
x ^= x >> 7;
x ^= x << 17;
(x >> 40) as f32 / (1u64 << 24) as f32 - 0.5
};
let make = |id: u64, v: Vec<f32>| MemoryRecord {
id,
chunk: String::new(),
embedding: v,
tier: MemoryTier::Working,
importance: 0.0,
access_count: 0,
last_accessed: 0.0,
created_at: 0.0,
source: MemorySource::User,
};
// Ordinary rows, a shorter one, an empty one and a zero vector; and
// enough rows to take the parallel path too.
for n in [5usize, 5000] {
let mut recs: Vec<MemoryRecord> = (0..n as u64)
.map(|i| make(i, (0..37).map(|_| next()).collect()))
.collect();
recs.push(make(9_000, (0..20).map(|_| next()).collect()));
recs.push(make(9_001, Vec::new()));
recs.push(make(9_002, vec![0.0; 37]));
let refs: Vec<&MemoryRecord> = recs.iter().collect();
for _ in 0..5 {
let q: Vec<f32> = (0..37).map(|_| next()).collect();
let expected = (1.0
- refs
.iter()
.map(|r| ImportanceScorer::cosine_similarity(&q, &r.embedding))
.fold(f32::NEG_INFINITY, f32::max))
.clamp(0.0, 1.0);
let got = ImportanceScorer::score_surprise(&q, &refs);
assert!((got - expected).abs() < 1e-5, "n={n}: {got} vs {expected}");
}
}
assert_eq!(
ImportanceScorer::score_surprise(&[0.0; 4], &[&make(1, vec![1.0; 4])]),
1.0
);
}
// Helper: build a simple normalised embedding of given dimension.
fn unit_vec(dim: usize, hot: usize) -> Vec<f32> {
let mut v = vec![0.0f32; dim];
@@ -622,44 +419,13 @@ mod tests {
// ---------------------------------------------------------------------------
// 2. Add memory — basic
// ---------------------------------------------------------------------------
/// add_trusted_memory(TrustedSource::Correction) must actually produce a
/// MemorySource::Correction record — the only way to reach that elevated
/// classification, since add_memory's UntrustedSource has no such variant.
#[test]
fn test_add_trusted_memory_sets_correction_source() {
let mut engine = ConsolidationEngine::new(ConsolidationConfig::default());
let id = engine.add_trusted_memory(
"verified correction".to_string(),
unit_vec(4, 0),
TrustedSource::Correction,
0.0,
);
let rec = engine.get_by_id(id).unwrap();
assert_eq!(rec.source, MemorySource::Correction);
}
/// add_trusted_memory(TrustedSource::System) must produce a
/// MemorySource::System record.
#[test]
fn test_add_trusted_memory_sets_system_source() {
let mut engine = ConsolidationEngine::new(ConsolidationConfig::default());
let id = engine.add_trusted_memory(
"bootstrap text".to_string(),
unit_vec(4, 0),
TrustedSource::System,
0.0,
);
let rec = engine.get_by_id(id).unwrap();
assert_eq!(rec.source, MemorySource::System);
}
#[test]
fn test_add_memory_basic() {
let mut engine = ConsolidationEngine::new(ConsolidationConfig::default());
let id = engine.add_memory(
"Hello world".to_string(),
unit_vec(4, 0),
UntrustedSource::User,
MemorySource::User,
1_000_000.0,
);
assert_eq!(id, 0);
@@ -687,7 +453,7 @@ mod tests {
#[test]
fn test_importance_scorer_surprise_identical() {
let emb = unit_vec(4, 0);
let existing = [MemoryRecord {
let existing = vec![MemoryRecord {
id: 0,
chunk: "existing".to_string(),
embedding: emb.clone(),
@@ -698,8 +464,7 @@ mod tests {
created_at: 0.0,
source: MemorySource::User,
}];
let existing_refs: Vec<&MemoryRecord> = existing.iter().collect();
let score = ImportanceScorer::score_surprise(&emb, &existing_refs);
let score = ImportanceScorer::score_surprise(&emb, &existing);
assert!(score < 0.01, "expected ~0.0, got {score}");
}
@@ -727,20 +492,23 @@ mod tests {
fn test_importance_scorer_length() {
assert!((ImportanceScorer::score_length("")).abs() < f32::EPSILON);
// 50 words → 0.5
let fifty_words = std::iter::repeat_n("word", 50)
let fifty_words = std::iter::repeat("word")
.take(50)
.collect::<Vec<_>>()
.join(" ");
let s50 = ImportanceScorer::score_length(&fifty_words);
assert!((s50 - 0.5).abs() < 1e-5, "expected 0.5, got {s50}");
// 100 words → 1.0
let hundred_words = std::iter::repeat_n("word", 100)
let hundred_words = std::iter::repeat("word")
.take(100)
.collect::<Vec<_>>()
.join(" ");
assert_eq!(ImportanceScorer::score_length(&hundred_words), 1.0);
// 200 words → still 1.0 (clamped)
let two_hundred = std::iter::repeat_n("word", 200)
let two_hundred = std::iter::repeat("word")
.take(200)
.collect::<Vec<_>>()
.join(" ");
assert_eq!(ImportanceScorer::score_length(&two_hundred), 1.0);
@@ -814,11 +582,9 @@ mod tests {
// ---------------------------------------------------------------------------
#[test]
fn test_consolidate_eviction_working() {
let cfg = ConsolidationConfig {
working_capacity: 3,
working_to_episodic_threshold: 2.0, // never promote in this test
..Default::default()
};
let mut cfg = ConsolidationConfig::default();
cfg.working_capacity = 3;
cfg.working_to_episodic_threshold = 2.0; // never promote in this test
let mut engine = ConsolidationEngine::new(cfg);
// Add 5 records; all have very low importance so none get promoted.
@@ -826,7 +592,7 @@ mod tests {
let id = engine.add_memory(
"x".to_string(),
unit_vec(4, i as usize),
UntrustedSource::User,
MemorySource::User,
i as f64,
);
// Force low importance so promotion threshold is not crossed.
@@ -859,10 +625,10 @@ mod tests {
let cfg = ConsolidationConfig::default();
let mut engine = ConsolidationEngine::new(cfg);
let id = engine.add_trusted_memory(
let id = engine.add_memory(
"important memory".to_string(),
unit_vec(4, 0),
TrustedSource::Correction,
MemorySource::Correction,
0.0,
);
// Force importance above threshold.
@@ -895,7 +661,7 @@ mod tests {
let id = engine.add_memory(
"frequently accessed".to_string(),
unit_vec(4, 0),
UntrustedSource::User,
MemorySource::User,
0.0,
);
@@ -923,12 +689,7 @@ mod tests {
#[test]
fn test_access_memory_reactivation() {
let mut engine = ConsolidationEngine::new(ConsolidationConfig::default());
let id = engine.add_memory(
"chunk".to_string(),
unit_vec(4, 0),
UntrustedSource::User,
0.0,
);
let id = engine.add_memory("chunk".to_string(), unit_vec(4, 0), MemorySource::User, 0.0);
engine.access_memory(id, 5000.0);
let rec = engine.get_by_id(id).unwrap();
@@ -949,11 +710,11 @@ mod tests {
let mut engine = ConsolidationEngine::new(ConsolidationConfig::default());
// 2 Working
engine.add_memory("w1".to_string(), unit_vec(4, 0), UntrustedSource::User, 0.0);
engine.add_memory("w2".to_string(), unit_vec(4, 1), UntrustedSource::User, 0.0);
engine.add_memory("w1".to_string(), unit_vec(4, 0), MemorySource::User, 0.0);
engine.add_memory("w2".to_string(), unit_vec(4, 1), MemorySource::User, 0.0);
// 1 Episodic (manually set)
let id_e = engine.add_memory("e1".to_string(), unit_vec(4, 2), UntrustedSource::User, 0.0);
let id_e = engine.add_memory("e1".to_string(), unit_vec(4, 2), MemorySource::User, 0.0);
engine
.records
.iter_mut()
@@ -962,7 +723,7 @@ mod tests {
.tier = MemoryTier::Episodic;
// 1 Semantic (manually set)
let id_s = engine.add_memory("s1".to_string(), unit_vec(4, 3), UntrustedSource::User, 0.0);
let id_s = engine.add_memory("s1".to_string(), unit_vec(4, 3), MemorySource::User, 0.0);
engine
.records
.iter_mut()
@@ -981,11 +742,9 @@ mod tests {
// ---------------------------------------------------------------------------
#[test]
fn test_consolidate_episodic_eviction() {
let cfg = ConsolidationConfig {
episodic_capacity: 3,
working_to_episodic_threshold: 2.0, // never auto-promote from Working
..Default::default()
};
let mut cfg = ConsolidationConfig::default();
cfg.episodic_capacity = 3;
cfg.working_to_episodic_threshold = 2.0; // never auto-promote from Working
let mut engine = ConsolidationEngine::new(cfg);
// Seed 5 records directly in Episodic.
@@ -993,7 +752,7 @@ mod tests {
let id = engine.add_memory(
"episodic chunk".to_string(),
unit_vec(4, i as usize),
UntrustedSource::User,
MemorySource::User,
i as f64,
);
let rec = engine.records.iter_mut().find(|r| r.id == id).unwrap();
crates/clawhdf5-agent/src/entity_extract.rs
+8 -14
View File
@@ -777,10 +777,8 @@ mod tests {
#[test]
fn test_tech_disabled() {
let config = ExtractorConfig {
extract_technology: false,
..Default::default()
};
let mut config = ExtractorConfig::default();
config.extract_technology = false;
let e = EntityExtractor::new(config);
let entities = e.extract("We use Rust and Docker.");
assert!(
@@ -849,10 +847,8 @@ mod tests {
#[test]
fn test_date_disabled() {
let config = ExtractorConfig {
extract_dates: false,
..Default::default()
};
let mut config = ExtractorConfig::default();
config.extract_dates = false;
let e = EntityExtractor::new(config);
let entities = e.extract("Released on 2024-03-19.");
assert!(
@@ -985,10 +981,8 @@ mod tests {
#[test]
fn test_confidence_filter() {
let config = ExtractorConfig {
min_confidence: 0.95,
..Default::default()
};
let mut config = ExtractorConfig::default();
config.min_confidence = 0.95;
let e = EntityExtractor::new(config);
// Only dates (0.95) and techs (0.9) should survive; 0.9 < 0.95 filters techs.
let entities = e.extract("We use Rust since 2024-01-01.");
@@ -1008,7 +1002,7 @@ mod tests {
fn test_batch_dedup() {
let e = default_extractor();
let texts = ["We use Rust.", "Rust is fast.", "Also Rust for safety."];
let entities = e.extract_batch(&texts);
let entities = e.extract_batch(&texts.iter().map(|s| *s).collect::<Vec<_>>());
let rust_count = entities.iter().filter(|x| x.text == "Rust").count();
assert_eq!(rust_count, 1, "Rust should appear exactly once after dedup");
}
@@ -1017,7 +1011,7 @@ mod tests {
fn test_batch_multiple_types() {
let e = default_extractor();
let texts = ["Deploy with Docker.", "We merged last week."];
let entities = e.extract_batch(&texts);
let entities = e.extract_batch(&texts.iter().map(|s| *s).collect::<Vec<_>>());
assert!(
entities
.iter()
crates/clawhdf5-agent/src/hybrid.rs
+38 -235
View File
@@ -28,69 +28,39 @@ use crate::vector_search;
pub fn hybrid_search(
query_embedding: &[f32],
query_text: &str,
vectors: &(impl crate::vector_search::VectorSet + Sync + ?Sized),
chunks: &[String],
vectors: &[Vec<f32>],
_chunks: &[String],
tombstones: &[u8],
bm25_index: &BM25Index,
vector_weight: f32,
keyword_weight: f32,
k: usize,
) -> Vec<(usize, f32)> {
hybrid_search_fused(
query_embedding,
query_text,
vectors,
chunks,
tombstones,
bm25_index,
Fusion::Weighted {
vector: vector_weight,
keyword: keyword_weight,
},
k,
)
}
/// [`hybrid_search`] with the fusion method chosen explicitly.
#[allow(clippy::too_many_arguments)]
pub fn hybrid_search_fused(
query_embedding: &[f32],
query_text: &str,
vectors: &(impl crate::vector_search::VectorSet + Sync + ?Sized),
_chunks: &[String],
tombstones: &[u8],
bm25_index: &BM25Index,
fusion: Fusion,
k: usize,
) -> Vec<(usize, f32)> {
// Get raw scores from both systems. Request all results so normalization
// covers the full distribution.
let vec_scores = exact_vector_scores(query_embedding, vectors, tombstones);
let kw_scores = bm25_index.scores(query_text);
fuse(vec_scores, kw_scores, fusion, k)
}
/// Cosine similarity of `query_embedding` to every vector whose `skip` byte is
/// 0 (a tombstone, or any other exclusion mask). Parallel above 10K vectors
/// when the `parallel` feature is on.
pub fn exact_vector_scores(
query_embedding: &[f32],
vectors: &(impl crate::vector_search::VectorSet + Sync + ?Sized),
skip: &[u8],
) -> Vec<(usize, f32)> {
// Use parallel search when rayon feature is enabled and vector count > 10K.
let vec_scores = {
#[cfg(feature = "parallel")]
{
if vectors.count() > 10_000 {
return vector_search::parallel_cosine_batch(
if vectors.len() > 10_000 {
vector_search::parallel_cosine_batch(
query_embedding,
vectors,
skip,
vectors.count(),
);
tombstones,
vectors.len(),
)
} else {
vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
}
}
vector_search::cosine_similarity_batch(query_embedding, vectors, skip)
#[cfg(not(feature = "parallel"))]
{
vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
}
};
let kw_scores = bm25_index.search(query_text, vectors.len());
merge_vector_keyword(vec_scores, kw_scores, vector_weight, keyword_weight, k)
}
/// Merge pre-computed vector-similarity and keyword scores into a single ranking.
@@ -106,120 +76,29 @@ pub fn merge_vector_keyword(
keyword_weight: f32,
k: usize,
) -> Vec<(usize, f32)> {
fuse(
vec_scores,
kw_scores,
Fusion::Weighted {
vector: vector_weight,
keyword: keyword_weight,
},
k,
)
}
/// How the vector and keyword stages are combined into one ranking.
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum Fusion {
/// Min-max normalise each stage over its own candidates, then take a
/// weighted sum. Uses the *scores*, so a stage that separates its
/// candidates sharply keeps that separation — and a stage whose candidates
/// are all near-identical contributes little.
Weighted {
/// Weight on the vector stage.
vector: f32,
/// Weight on the keyword stage.
keyword: f32,
},
/// Reciprocal rank fusion: each stage contributes `1 / (k + rank)`,
/// ignoring score magnitudes entirely. Robust when the two stages'
/// scores aren't comparable, at the cost of discarding confidence.
Rrf {
/// The rank-damping constant; 60 is the value from the original paper.
k: f32,
},
}
impl Default for Fusion {
fn default() -> Self {
DEFAULT_FUSION
}
}
/// The fusion `hybrid_search` uses unless told otherwise.
///
/// The weights are not a guess: a sweep of every 0.1 step over the full
/// LongMemEval haystack (500 questions, real MiniLM embeddings) found the
/// long-standing 0.7/0.3 default *strictly dominated* — 0.4/0.6 is better at
/// Hit@1, Hit@5, Hit@10 and MRR, at both turn and session granularity. See
/// `BENCHMARKS.md`, "Weight sweep".
pub const DEFAULT_FUSION: Fusion = Fusion::Weighted {
vector: 0.4,
keyword: 0.6,
};
/// Combine one ranked candidate list from each stage into a single top-`k`.
///
/// Neither list need be sorted; both are consumed.
pub fn fuse(
vec_scores: Vec<(usize, f32)>,
kw_scores: Vec<(usize, f32)>,
fusion: Fusion,
k: usize,
) -> Vec<(usize, f32)> {
let mut merged: HashMap<usize, f32> = HashMap::new();
match fusion {
Fusion::Weighted { vector, keyword } => {
// Normalize each set to [0, 1].
for (idx, score) in &normalize_scores(&vec_scores) {
*merged.entry(*idx).or_insert(0.0) += vector * score;
}
for (idx, score) in &normalize_scores(&kw_scores) {
*merged.entry(*idx).or_insert(0.0) += keyword * score;
}
}
Fusion::Rrf { k: damping } => {
for mut stage in [vec_scores, kw_scores] {
// Rank 1 is the best score. Ties break by index so a stage's
// contribution doesn't depend on the candidate order it
// happened to be produced in.
stage.sort_by(|a, b| {
b.1.partial_cmp(&a.1)
.unwrap_or(std::cmp::Ordering::Equal)
.then(a.0.cmp(&b.0))
});
for (rank, (idx, _)) in stage.iter().enumerate() {
*merged.entry(*idx).or_insert(0.0) += 1.0 / (damping + (rank + 1) as f32);
}
}
let vec_normalized = normalize_scores(&vec_scores);
let kw_normalized = normalize_scores(&kw_scores);
// Merge scores with weights.
let mut merged: HashMap<usize, f32> = HashMap::new();
for (idx, score) in &vec_normalized {
*merged.entry(*idx).or_insert(0.0) += vector_weight * score;
}
for (idx, score) in &kw_normalized {
*merged.entry(*idx).or_insert(0.0) += keyword_weight * score;
}
let mut results: Vec<(usize, f32)> = merged.into_iter().collect();
// Index tie-break: `merged` is a HashMap, so without it the ties that
// survive differ from run to run.
let by_score_then_id = |a: &(usize, f32), b: &(usize, f32)| {
b.1.partial_cmp(&a.1)
.unwrap_or(std::cmp::Ordering::Equal)
.then(a.0.cmp(&b.0))
};
// Only the top k are wanted: partition them out, then order just those,
// instead of sorting every candidate (the keyword side can be the corpus).
if k == 0 {
return Vec::new();
}
if results.len() > k {
results.select_nth_unstable_by(k - 1, by_score_then_id);
results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
results.truncate(k);
}
results.sort_by(by_score_then_id);
results
}
/// Normalize a set of scores to the [0, 1] range using min-max normalization.
///
/// If all scores are identical there is no spread to normalise: each entry
/// gets 1.0 when that score is positive (all equally the best match) and 0.0
/// otherwise (nothing matched).
/// If all scores are identical, returns 0.0 for each entry.
fn normalize_scores(scores: &[(usize, f32)]) -> Vec<(usize, f32)> {
if scores.is_empty() {
return Vec::new();
@@ -233,13 +112,7 @@ fn normalize_scores(scores: &[(usize, f32)]) -> Vec<(usize, f32)> {
let range = max - min;
if range == 0.0 {
// All candidates scored the same (including the single-candidate
// case), so min-max has no spread to work with. They are all equally
// the best match if that score is positive, and all non-matches
// otherwise. This used to return 0.0 unconditionally, which erased a
// lone perfect match from the fused score.
let level = if max > 0.0 { 1.0 } else { 0.0 };
return scores.iter().map(|(idx, _)| (*idx, level)).collect();
return scores.iter().map(|(idx, _)| (*idx, 0.0)).collect();
}
scores
@@ -273,7 +146,7 @@ fn normalize_scores(scores: &[(usize, f32)]) -> Vec<(usize, f32)> {
pub fn rrf_hybrid_search(
query_embedding: &[f32],
query_text: &str,
vectors: &(impl crate::vector_search::VectorSet + Sync + ?Sized),
vectors: &[Vec<f32>],
_chunks: &[String],
tombstones: &[u8],
bm25_index: &BM25Index,
@@ -285,12 +158,12 @@ pub fn rrf_hybrid_search(
let mut vec_scores = {
#[cfg(feature = "parallel")]
{
if vectors.count() > 10_000 {
if vectors.len() > 10_000 {
vector_search::parallel_cosine_batch(
query_embedding,
vectors,
tombstones,
vectors.count(),
vectors.len(),
)
} else {
vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
@@ -301,7 +174,7 @@ pub fn rrf_hybrid_search(
vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
}
};
let mut kw_scores = bm25_index.search(query_text, vectors.count());
let mut kw_scores = bm25_index.search(query_text, vectors.len());
// Sort both lists descending so rank 1 = best.
vec_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
@@ -451,80 +324,10 @@ mod tests {
#[test]
fn normalize_scores_single() {
// A lone positive score is the best match there is, not a non-match.
let result = normalize_scores(&[(0, 5.0)]);
assert_eq!(result.len(), 1);
assert_eq!(result[0].1, 1.0);
}
#[test]
fn default_fusion_is_the_tuned_operating_point() {
// A sweep over the full LongMemEval haystack found 0.7/0.3 strictly
// dominated by 0.4/0.6 (BENCHMARKS.md). This guards the finding
// against being quietly undone.
assert_eq!(
DEFAULT_FUSION,
Fusion::Weighted {
vector: 0.4,
keyword: 0.6
}
);
}
#[test]
fn rrf_rewards_agreement_between_the_stages_and_ignores_magnitudes() {
// Doc 1 is second-best in both stages; doc 0 is best in one and absent
// from the other. RRF prefers the doc both stages liked.
let vec_scores = vec![(0, 100.0), (1, 0.9)];
let kw_scores = vec![(2, 5.0), (1, 4.9)];
let ranked = fuse(vec_scores, kw_scores, Fusion::Rrf { k: 60.0 }, 3);
assert_eq!(ranked[0].0, 1, "{ranked:?}");
// Scaling one stage's scores cannot change an RRF ranking, only the
// order within that stage can.
let a = fuse(
vec![(0, 1.0), (1, 0.5)],
vec![(1, 2.0), (0, 1.0)],
Fusion::Rrf { k: 60.0 },
2,
);
let b = fuse(
vec![(0, 1e6), (1, -3.0)],
vec![(1, 0.002), (0, 0.001)],
Fusion::Rrf { k: 60.0 },
2,
);
assert_eq!(
a.iter().map(|r| r.0).collect::<Vec<_>>(),
b.iter().map(|r| r.0).collect::<Vec<_>>()
);
}
#[test]
fn merge_top_k_matches_a_full_sort() {
// Many ties (scores repeat) so the index tie-break is exercised.
let vec_scores: Vec<(usize, f32)> = (0..300).map(|i| (i, ((i * 7) % 13) as f32)).collect();
let kw_scores: Vec<(usize, f32)> = (100..500).map(|i| (i, ((i * 5) % 11) as f32)).collect();
let everything =
merge_vector_keyword(vec_scores.clone(), kw_scores.clone(), 0.7, 0.3, 10_000);
assert_eq!(everything.len(), 500);
assert!(
everything
.windows(2)
.all(|w| { w[0].1 > w[1].1 || (w[0].1 == w[1].1 && w[0].0 < w[1].0) })
);
for k in [0, 1, 7, 50, 499, 500, 501] {
let top = merge_vector_keyword(vec_scores.clone(), kw_scores.clone(), 0.7, 0.3, k);
assert_eq!(top, everything[..k.min(500)], "k = {k}");
}
}
#[test]
fn normalize_scores_all_equal() {
let matched = normalize_scores(&[(0, 0.4), (1, 0.4)]);
assert!(matched.iter().all(|(_, s)| *s == 1.0));
let unmatched = normalize_scores(&[(0, 0.0), (1, 0.0)]);
assert!(unmatched.iter().all(|(_, s)| *s == 0.0));
// Single score normalizes to 0.0 (range is 0)
assert_eq!(result[0].1, 0.0);
}
#[test]
crates/clawhdf5-agent/src/knowledge.rs
+18 -228
View File
@@ -50,9 +50,6 @@ impl RelationType {
pub struct Entity {
pub id: u64,
pub name: String,
/// Lowercased `name`, cached at construction time to avoid re-allocating
/// and re-lowercasing on every entity-resolution scan.
pub name_lower: String,
pub entity_type: String,
/// Index into the memory embeddings array, or -1 if none.
pub embedding_idx: i64,
@@ -72,7 +69,6 @@ impl Default for Entity {
Self {
id: 0,
name: String::new(),
name_lower: String::new(),
entity_type: String::new(),
embedding_idx: -1,
properties: HashMap::new(),
@@ -155,95 +151,6 @@ fn levenshtein(a: &str, b: &str) -> usize {
prev[nb]
}
// ---------------------------------------------------------------------------
// AdjacencyIndex
// ---------------------------------------------------------------------------
/// Adjacency index over a snapshot of `entities`/`relations`: an entity-id ->
/// entities-slice-index map, and an entity-id -> relation-indices map (edges
/// touching that entity as either source or target).
///
/// Cached on `KnowledgeCache` and checked against a fingerprint of the graph
/// on every use ([`graph_fingerprint`]). entities/relations are plain `pub`
/// `Vec`s that get changed directly (e.g. `schema.rs`'s load path bypasses
/// `add_entity`/`add_relation`), so the cache cannot rely on being told about
/// changes; the fingerprint notices any of them. Rebuilding it on every
/// traversal instead made a 2-hop BFS over 1K entities 6.5x slower than the
/// scan it replaced (24 -> 155 µs; `BENCHMARKS.md`, "Knowledge Graph").
struct AdjacencyIndex {
entity_index: HashMap<u64, usize>,
by_entity: HashMap<u64, Vec<usize>>,
}
impl AdjacencyIndex {
fn build(entities: &[Entity], relations: &[Relation]) -> Self {
let mut entity_index = HashMap::with_capacity(entities.len());
for (i, e) in entities.iter().enumerate() {
entity_index.insert(e.id, i);
}
let mut by_entity: HashMap<u64, Vec<usize>> = HashMap::new();
for (i, r) in relations.iter().enumerate() {
by_entity.entry(r.src).or_default().push(i);
if r.tgt != r.src {
by_entity.entry(r.tgt).or_default().push(i);
}
}
Self {
entity_index,
by_entity,
}
}
/// Indices into `relations` of every edge touching `entity_id`.
fn relations_touching(&self, entity_id: u64) -> &[usize] {
self.by_entity
.get(&entity_id)
.map(|v| v.as_slice())
.unwrap_or(&[])
}
}
/// A hash of everything [`AdjacencyIndex`] depends on — each entity's id and
/// position, each relation's endpoints and position. One linear pass, no
/// allocation: far cheaper than building the index, which hashes the same
/// values into two maps.
fn graph_fingerprint(entities: &[Entity], relations: &[Relation]) -> u64 {
// splitmix64-style mixing; order matters, so positions are covered.
fn mix(h: u64, v: u64) -> u64 {
let mut z = (h ^ v).wrapping_add(0x9E37_79B9_7F4A_7C15);
z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
z ^ (z >> 31)
}
let mut h = mix(entities.len() as u64, relations.len() as u64);
for e in entities {
h = mix(h, e.id);
}
for r in relations {
h = mix(mix(h, r.src), r.tgt);
}
h
}
/// The cached [`AdjacencyIndex`] and the fingerprint it was built for.
/// Cloning a `KnowledgeCache` starts the clone with an empty cache.
#[derive(Default)]
struct AdjacencyCache(std::sync::Mutex<Option<(u64, std::sync::Arc<AdjacencyIndex>)>>);
impl Clone for AdjacencyCache {
fn clone(&self) -> Self {
Self::default()
}
}
impl std::fmt::Debug for AdjacencyCache {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.write_str("AdjacencyCache")
}
}
// ---------------------------------------------------------------------------
// KnowledgeCache
// ---------------------------------------------------------------------------
@@ -256,7 +163,6 @@ pub struct KnowledgeCache {
pub alias_strings: Vec<String>,
pub alias_entity_ids: Vec<i64>,
next_entity_id: u64,
adjacency: AdjacencyCache,
}
impl KnowledgeCache {
@@ -267,7 +173,6 @@ impl KnowledgeCache {
alias_strings: Vec::new(),
alias_entity_ids: Vec::new(),
next_entity_id: 0,
adjacency: AdjacencyCache::default(),
}
}
@@ -278,29 +183,9 @@ impl KnowledgeCache {
alias_strings: Vec::new(),
alias_entity_ids: Vec::new(),
next_entity_id: next_id,
adjacency: AdjacencyCache::default(),
}
}
/// The adjacency index for the graph as it is now: the cached one if the
/// graph's fingerprint still matches, otherwise rebuilt and cached.
fn adjacency_index(&self) -> std::sync::Arc<AdjacencyIndex> {
let fp = graph_fingerprint(&self.entities, &self.relations);
let mut slot = self
.adjacency
.0
.lock()
.unwrap_or_else(std::sync::PoisonError::into_inner);
if let Some((cached_fp, idx)) = slot.as_ref()
&& *cached_fp == fp
{
return idx.clone();
}
let idx = std::sync::Arc::new(AdjacencyIndex::build(&self.entities, &self.relations));
*slot = Some((fp, idx.clone()));
idx
}
// -----------------------------------------------------------------------
// Entity management
// -----------------------------------------------------------------------
@@ -313,7 +198,6 @@ impl KnowledgeCache {
self.entities.push(Entity {
id,
name: name.to_owned(),
name_lower: name.to_lowercase(),
entity_type: entity_type.to_owned(),
embedding_idx,
properties: HashMap::new(),
@@ -426,22 +310,16 @@ impl KnowledgeCache {
) -> (u64, bool) {
let lower_name = name.to_lowercase();
// Search for the closest existing entity, short-circuiting on an
// exact match since no closer candidate can exist.
let mut best: Option<(u64, usize)> = None;
for e in &self.entities {
let dist = levenshtein(&lower_name, &e.name_lower);
if dist > max_distance {
continue;
}
if dist == 0 {
best = Some((e.id, dist));
break;
}
if best.is_none_or(|(_, best_dist)| dist < best_dist) {
best = Some((e.id, dist));
}
}
// Search for the closest existing entity.
let best = self
.entities
.iter()
.map(|e| {
let dist = levenshtein(&lower_name, &e.name.to_lowercase());
(e.id, dist)
})
.filter(|&(_, dist)| dist <= max_distance)
.min_by_key(|&(_, dist)| dist);
if let Some((id, _)) = best {
return (id, false);
@@ -459,7 +337,6 @@ impl KnowledgeCache {
/// together with their discovered depth. The seed entity itself is NOT
/// included. Traversal follows both outgoing and incoming relation edges.
pub fn bfs_neighbors(&self, entity_id: u64, max_depth: usize) -> Vec<(Entity, usize)> {
let idx = self.adjacency_index();
let mut visited: HashSet<u64> = HashSet::new();
let mut queue: VecDeque<(u64, usize)> = VecDeque::new();
let mut results: Vec<(Entity, usize)> = Vec::new();
@@ -472,13 +349,11 @@ impl KnowledgeCache {
continue;
}
// Collect neighbour IDs from outgoing and incoming edges touching
// this node only, instead of scanning every relation in the graph.
let neighbours: Vec<u64> = idx
.relations_touching(current_id)
// Collect neighbour IDs from outgoing and incoming edges.
let neighbours: Vec<u64> = self
.relations
.iter()
.filter_map(|&i| {
let r = &self.relations[i];
.filter_map(|r| {
if r.src == current_id {
Some(r.tgt)
} else if r.tgt == current_id {
@@ -491,9 +366,9 @@ impl KnowledgeCache {
for neighbour_id in neighbours {
if visited.insert(neighbour_id)
&& let Some(&entity_idx) = idx.entity_index.get(&neighbour_id)
&& let Some(entity) = self.get_entity(neighbour_id)
{
results.push((self.entities[entity_idx].clone(), depth + 1));
results.push((entity.clone(), depth + 1));
queue.push_back((neighbour_id, depth + 1));
}
}
@@ -564,7 +439,6 @@ impl KnowledgeCache {
min_activation: f32,
max_steps: usize,
) -> Vec<(u64, f32)> {
let idx = self.adjacency_index();
let mut activation: HashMap<u64, f32> = HashMap::new();
// Initialise seeds with activation 1.0.
@@ -587,10 +461,8 @@ impl KnowledgeCache {
let mut any_spread = false;
for (source_id, source_score) in current {
// Spread only to edges touching this node, instead of
// scanning every relation in the graph per active node.
for &rel_idx in idx.relations_touching(source_id) {
let rel = &self.relations[rel_idx];
// Spread to all neighbours via outgoing and incoming edges.
for rel in &self.relations {
let neighbour_id = if rel.src == source_id {
rel.tgt
} else if rel.tgt == source_id {
@@ -693,51 +565,6 @@ impl Default for KnowledgeCache {
mod tests {
use super::*;
#[test]
fn cached_adjacency_sees_direct_changes_to_the_graph() {
// The index is cached across traversals, but entities/relations are
// pub Vecs anyone can edit; every kind of edit must be seen.
let mut kg = KnowledgeCache::new();
let a = kg.add_entity("a", "t", -1);
let b = kg.add_entity("b", "t", -1);
let c = kg.add_entity("c", "t", -1);
kg.add_relation(a, b, "r", 1.0);
let ids = |kg: &KnowledgeCache| -> Vec<u64> {
let mut v: Vec<u64> = kg.bfs_neighbors(a, 3).iter().map(|(e, _)| e.id).collect();
v.sort();
v
};
assert_eq!(ids(&kg), vec![b]);
assert_eq!(ids(&kg), vec![b], "cached index reused");
// Pushed directly, bypassing add_relation.
kg.relations.push(Relation {
src: b,
tgt: c,
..Relation::default()
});
assert_eq!(ids(&kg), vec![b, c]);
// Rewired in place: same lengths, different edge.
kg.relations[1].tgt = a;
assert_eq!(ids(&kg), vec![b]);
// Removed and replaced: same lengths again.
kg.relations.pop();
kg.relations.push(Relation {
src: a,
tgt: c,
..Relation::default()
});
assert_eq!(ids(&kg), vec![b, c]);
let act: Vec<u64> = kg
.spreading_activation(&[a], 0.5, 0.0, 2)
.iter()
.map(|(id, _)| *id)
.collect();
assert!(act.contains(&c));
}
// -----------------------------------------------------------------------
// Original tests — must remain passing
// -----------------------------------------------------------------------
@@ -1028,19 +855,6 @@ mod tests {
assert_eq!(id, orig_id);
}
/// An exact match must win even when a near-match with a smaller Levenshtein
/// distance-to-zero gap was scanned first — the early exit on dist == 0
/// must not skip past a later exact match.
#[test]
fn test_resolve_or_create_exact_match_beats_earlier_fuzzy_candidate() {
let mut cache = KnowledgeCache::new();
cache.add_entity("Alyce", "person", -1); // dist 1 from "Alice"
let exact_id = cache.add_entity("Alice", "person", -1); // dist 0
let (id, created) = cache.resolve_or_create("Alice", "person", -1, 2);
assert!(!created);
assert_eq!(id, exact_id);
}
#[test]
fn test_resolve_or_create_no_match_beyond_threshold() {
let mut cache = KnowledgeCache::new();
@@ -1221,30 +1035,6 @@ mod tests {
assert!(b_score.unwrap() > 0.0);
}
/// A self-loop relation (src == tgt) must be visited exactly once by the
/// adjacency index, matching the pre-index behavior of iterating
/// `self.relations` directly (each relation processed once regardless of
/// how many of its endpoints match the current node).
#[test]
fn test_spreading_activation_self_loop_not_double_counted() {
let mut cache = KnowledgeCache::new();
let a = cache.add_entity("A", "node", -1);
cache.add_relation(a, a, "self", 1.0);
let result = cache.spreading_activation(&[a], 0.5, 0.0001, 1);
let a_score = result
.iter()
.find(|&&(id, _)| id == a)
.map(|&(_, s)| s)
.unwrap();
// Seed activation (1.0) plus exactly one spread contribution
// (1.0 * weight 1.0 * decay 0.5), not two.
assert!(
(a_score - 1.5).abs() < 1e-5,
"expected 1.5 (one self-loop contribution), got {a_score}"
);
}
#[test]
fn test_spreading_activation_decay_reduces_signal() {
let mut cache = KnowledgeCache::new();
crates/clawhdf5-agent/src/lib.rs
+267 -1601
View File
File diff suppressed because it is too large Load Diff
crates/clawhdf5-agent/src/openclaw.rs
+129 -93
View File
@@ -1,12 +1,10 @@
//! A Markdown-oriented memory backend over [`crate::HDF5Memory`].
//! OpenClaw Integration Layer.
//!
//! Named for OpenClaw, whose workspace memory is Markdown, but **not an
//! OpenClaw plugin**: nothing here registers with OpenClaw, and the
//! integration it was written for never worked (see `docs/openclaw.md`).
//! Provides:
//! Bridge between OpenClaw agent gateway (Markdown + sqlite-vec) and the
//! clawhdf5 HDF5-backed memory backend. Provides:
//!
//! - [`MemoryBackend`] — search / read back / write / ingest / export.
//! - [`ClawhdfBackend`] — the HDF5-backed implementation.
//! - [`MemoryBackend`] — the trait OpenClaw implements against.
//! - [`ClawhdfBackend`] — concrete HDF5-backed implementation.
//! - [`MarkdownParser`] — splits Markdown into [`MarkdownSection`] records.
//! - [`MarkdownExporter`] — renders sections back to Markdown text.
@@ -15,8 +13,9 @@ use std::path::{Path, PathBuf};
use std::time::{SystemTime, UNIX_EPOCH};
use crate::{
AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, SearchOptions,
confidence::ConfidenceConfig, reranker::ReRankConfig,
AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry,
confidence::{ConfidenceConfig, ScoredResult, reject_low_confidence},
reranker::{ReRankConfig, RerankInput, rerank},
};
// ─────────────────────────────────────────────────────────────────────────────
@@ -63,8 +62,7 @@ pub struct BackendStats {
// MemoryBackend trait
// ─────────────────────────────────────────────────────────────────────────────
/// A Markdown-oriented memory backend: search, read back by path, write,
/// ingest and export.
/// Interface that OpenClaw uses to interact with a memory backend.
///
/// Implementors provide persistent storage, full-text + vector search,
/// Markdown ingestion / export, and statistics.
@@ -321,7 +319,7 @@ impl MarkdownExporter {
///
/// # Path mapping
///
/// Memories are addressed by file path (e.g. `"memory/user.md"`).
/// OpenClaw addresses memories by file path (e.g. `"memory/user.md"`).
/// Internally every [`MemoryEntry`] stores the originating path as its
/// `source_channel`. Section sub-paths are stored as
/// `"<path>::<heading>"`.
@@ -424,7 +422,7 @@ impl ClawhdfBackend {
// ── Compaction & Consolidation hooks (7.6) ────────────────────────────
/// Run a compaction cycle (decay, compaction, WAL flush).
/// Run a compaction cycle — called by OpenClaw during session compaction.
///
/// Sequence:
/// 1. `tick_session()` — apply Hebbian decay to all activation weights.
@@ -468,7 +466,7 @@ impl ClawhdfBackend {
let record = MemoryRecord {
id: i as u64,
chunk: cache.chunks[i].clone(),
embedding: cache.embeddings[i].to_vec(),
embedding: cache.embeddings[i].clone(),
tier: MemoryTier::Working,
importance: cache.activation_weights[i],
access_count: 0,
@@ -526,27 +524,67 @@ impl ClawhdfBackend {
impl MemoryBackend for ClawhdfBackend {
/// Search using hybrid vector + BM25 retrieval, then re-rank and
/// confidence-filter — [`HDF5Memory::search`] with both stages on.
/// confidence-filter.
fn search(
&mut self,
query_text: &str,
query_embedding: &[f32],
k: usize,
) -> Vec<MemorySearchResult> {
let options = SearchOptions::new(k)
.with_rerank(self.rerank_config)
.with_confidence(self.confidence_config.clone())
.at_time(Self::now_secs());
self.memory
.search(query_embedding, query_text, &options)
// 1. Hybrid retrieval (RRF-blended vector + BM25).
let candidates = k.saturating_mul(3).max(10);
let raw = self
.memory
.hybrid_search(query_embedding, query_text, 0.7, 0.3, candidates);
if raw.is_empty() {
return Vec::new();
}
let now = Self::now_secs();
// 2. Re-rank using temporal recency, source authority, Hebbian weight.
let rerank_inputs: Vec<RerankInput> = raw
.iter()
.map(|r| RerankInput {
index: r.index,
timestamp: r.timestamp,
source_channel: r.source_channel.clone(),
raw_activation: r.activation,
})
.collect();
let reranked = rerank(&rerank_inputs, &self.rerank_config, now);
// 3. Confidence rejection.
let scored: Vec<ScoredResult> = reranked
.iter()
.map(|r| ScoredResult {
index: r.index,
score: r.combined_score,
})
.collect();
let confident = reject_low_confidence(&scored, &self.confidence_config);
// 4. Map back to MemorySearchResult; preserve raw text via index lookup.
let raw_by_idx: HashMap<usize, &crate::SearchResult> =
raw.iter().map(|r| (r.index, r)).collect();
confident
.into_iter()
.map(|r| MemorySearchResult {
text: r.chunk,
score: r.score,
path: r.source_channel.clone(),
.take(k)
.filter_map(|sr| {
let r = raw_by_idx.get(&sr.index)?;
let path = r.source_channel.clone();
Some(MemorySearchResult {
text: r.chunk.clone(),
score: sr.score,
path: path.clone(),
line_range: None,
timestamp: Some(r.timestamp),
source: r.source_channel,
source: path,
})
})
.collect()
}
@@ -675,13 +713,11 @@ impl MemoryBackend for ClawhdfBackend {
let total_records = cache.count_active();
// A record saved without an embedding occupies a zero row, so "has an
// embedding" is "has a non-zero norm" rather than "row is non-empty".
let total_embeddings = cache
.norms
.embeddings
.iter()
.enumerate()
.filter(|(i, norm)| cache.tombstones[*i] == 0 && **norm > 0.0)
.filter(|(i, emb)| cache.tombstones[*i] == 0 && !emb.is_empty())
.count();
let file_size_bytes = std::fs::metadata(&self.hdf5_path)
@@ -712,69 +748,6 @@ impl MemoryBackend for ClawhdfBackend {
}
}
// ─────────────────────────────────────────────────────────────────────────────
// Ephemeral tier methods on ClawhdfBackend
// ─────────────────────────────────────────────────────────────────────────────
impl ClawhdfBackend {
/// Enable the ephemeral (in-memory only) working memory tier.
pub fn enable_ephemeral(&mut self, config: crate::ephemeral::EphemeralConfig) {
self.memory.enable_ephemeral(config);
}
/// Store a text value in ephemeral memory.
///
/// Returns an error string if the ephemeral tier has not been enabled.
pub fn ephemeral_set(
&mut self,
key: &str,
value: &str,
ttl_secs: Option<f64>,
) -> Result<(), String> {
match self.memory.ephemeral_mut() {
Some(s) => {
s.set_text(key, value, ttl_secs);
Ok(())
}
None => Err("ephemeral tier not enabled".to_string()),
}
}
/// Retrieve a text value from ephemeral memory.
///
/// Returns `None` if the tier is disabled, the key is absent, or the
/// entry has expired.
pub fn ephemeral_get(&mut self, key: &str) -> Option<String> {
self.memory
.ephemeral_mut()?
.get_text(key)
.map(|s| s.to_string())
}
/// Delete a key from ephemeral memory.
///
/// Returns `true` if the key existed and was removed.
pub fn ephemeral_delete(&mut self, key: &str) -> bool {
self.memory.ephemeral_mut().is_some_and(|s| s.delete(key))
}
/// Return a snapshot of ephemeral tier statistics, or `None` if the tier
/// is not enabled.
pub fn ephemeral_stats(&self) -> Option<crate::ephemeral::EphemeralStats> {
self.memory.ephemeral().map(|s| s.stats())
}
/// Promote frequently-accessed ephemeral entries to persistent HDF5 storage.
///
/// Entries with `access_count >= min_access_count` are moved from the
/// ephemeral store into the persistent cache. Returns the count promoted.
pub fn promote_ephemeral(&mut self, min_access_count: u32) -> Result<usize, String> {
self.memory
.promote_ephemeral(min_access_count)
.map_err(|e| e.to_string())
}
}
// ─────────────────────────────────────────────────────────────────────────────
// Tests
// ─────────────────────────────────────────────────────────────────────────────
@@ -1360,3 +1333,66 @@ mod tests {
assert!(out.starts_with("# Title"));
}
}
// ─────────────────────────────────────────────────────────────────────────────
// Ephemeral tier methods on ClawhdfBackend
// ─────────────────────────────────────────────────────────────────────────────
impl ClawhdfBackend {
/// Enable the ephemeral (in-memory only) working memory tier.
pub fn enable_ephemeral(&mut self, config: crate::ephemeral::EphemeralConfig) {
self.memory.enable_ephemeral(config);
}
/// Store a text value in ephemeral memory.
///
/// Returns an error string if the ephemeral tier has not been enabled.
pub fn ephemeral_set(
&mut self,
key: &str,
value: &str,
ttl_secs: Option<f64>,
) -> Result<(), String> {
match self.memory.ephemeral_mut() {
Some(s) => {
s.set_text(key, value, ttl_secs);
Ok(())
}
None => Err("ephemeral tier not enabled".to_string()),
}
}
/// Retrieve a text value from ephemeral memory.
///
/// Returns `None` if the tier is disabled, the key is absent, or the
/// entry has expired.
pub fn ephemeral_get(&mut self, key: &str) -> Option<String> {
self.memory
.ephemeral_mut()?
.get_text(key)
.map(|s| s.to_string())
}
/// Delete a key from ephemeral memory.
///
/// Returns `true` if the key existed and was removed.
pub fn ephemeral_delete(&mut self, key: &str) -> bool {
self.memory.ephemeral_mut().is_some_and(|s| s.delete(key))
}
/// Return a snapshot of ephemeral tier statistics, or `None` if the tier
/// is not enabled.
pub fn ephemeral_stats(&self) -> Option<crate::ephemeral::EphemeralStats> {
self.memory.ephemeral().map(|s| s.stats())
}
/// Promote frequently-accessed ephemeral entries to persistent HDF5 storage.
///
/// Entries with `access_count >= min_access_count` are moved from the
/// ephemeral store into the persistent cache. Returns the count promoted.
pub fn promote_ephemeral(&mut self, min_access_count: u32) -> Result<usize, String> {
self.memory
.promote_ephemeral(min_access_count)
.map_err(|e| e.to_string())
}
}
crates/clawhdf5-agent/src/provenance.rs
+1 -18
View File
@@ -2,7 +2,7 @@
//!
//! Records the origin, authorship, and a content hash of every memory chunk
//! so the system can detect *accidental* corruption and trace data lineage.
//! The hash is unkeyed (FNV-1a) — this is not a tamper-evidence or
//! The hash is unkeyed (see [`fnv1a_64`]) — this is not a tamper-evidence or
//! authenticity guarantee.
use std::collections::HashMap;
@@ -105,23 +105,6 @@ impl ProvenanceStore {
self.records.insert(provenance.record_id, provenance);
}
/// Renumber records after the store was compacted. `index_map[old]` is
/// the record's new id, or `None` if it was removed. Without this, every
/// surviving record's hash ends up filed under some other record's id and
/// the next integrity check reports a bogus mismatch.
pub fn remap(&mut self, index_map: &[Option<usize>]) {
let old = std::mem::take(&mut self.records);
for (old_id, mut prov) in old {
let new_id = usize::try_from(old_id)
.ok()
.and_then(|i| index_map.get(i).copied().flatten());
if let Some(new_id) = new_id {
prov.record_id = new_id as u64;
self.records.insert(new_id as u64, prov);
}
}
}
/// Retrieve by record ID.
pub fn get(&self, record_id: u64) -> Option<&MemoryProvenance> {
self.records.get(&record_id)
crates/clawhdf5-agent/src/query_expand.rs
+8 -162
View File
@@ -6,11 +6,6 @@
//! - Temporal expansion (time-related rewrites)
//! - Morphological variants (stemming-like transforms)
//! - Knowledge graph expansion (entity aliases and neighbors)
//!
//! The morphological rules are crude suffix swaps, so some variants are not
//! words ("during" -> "dured"). That is tolerable for a BM25 stage, which
//! simply finds no postings for a nonsense term, but it means expansion is not
//! free: measure before enabling it on a retrieval path.
use crate::knowledge::KnowledgeCache;
@@ -345,85 +340,18 @@ fn contains_phrase(text: &str, phrase: &str) -> bool {
/// Replace a phrase in `text` case-insensitively, preserving surrounding case.
fn replace_word_case_insensitive(text: &str, from: &str, to: &str) -> String {
replace_first(text, from, to, MatchKind::WholeWord)
case_insensitive_replace(text, from, to)
}
fn case_insensitive_replace(text: &str, from: &str, to: &str) -> String {
replace_first(text, from, to, MatchKind::Substring)
}
/// Whether a match may fall inside a larger word.
#[derive(Clone, Copy, PartialEq)]
enum MatchKind {
/// Match anywhere, including inside another word.
Substring,
/// Match only when both ends sit on a word boundary.
WholeWord,
}
/// Replace the first case-insensitive match of `from` in `text` with `to`.
///
/// Matching walks the *original* string rather than a lowercased copy. The
/// previous implementation searched `text.to_lowercase()` and then sliced
/// `text` with the offsets it found, which only holds while lowercasing
/// preserves byte length. It does not: Turkish `İ` (2 bytes) lowercases to
/// `i` + U+0307 (3 bytes), so every later offset was wrong — silently
/// corrupting the output, or panicking when an offset landed inside a
/// character or past the end. `"İ AI"` was enough to panic.
fn replace_first(text: &str, from: &str, to: &str, kind: MatchKind) -> String {
match find_case_insensitive(text, from, kind) {
Some((start, end)) => {
let mut out = String::with_capacity(text.len() - (end - start) + to.len());
out.push_str(&text[..start]);
out.push_str(to);
out.push_str(&text[end..]);
out
let lower = text.to_lowercase();
let lower_from = from.to_lowercase();
if let Some(pos) = lower.find(&lower_from) {
let end = pos + from.len();
format!("{}{}{}", &text[..pos], to, &text[end..])
} else {
text.to_string()
}
None => text.to_string(),
}
}
/// Byte range of the first case-insensitive match of `needle` in `haystack`.
fn find_case_insensitive(haystack: &str, needle: &str, kind: MatchKind) -> Option<(usize, usize)> {
if needle.is_empty() {
return None;
}
let lowered: Vec<char> = needle.chars().flat_map(char::to_lowercase).collect();
let is_word = |c: char| c.is_alphanumeric() || c == '_';
for (start, _) in haystack.char_indices() {
if kind == MatchKind::WholeWord
&& haystack[..start].chars().next_back().is_some_and(is_word)
{
continue; // mid-word: "ai" inside "training"
}
let mut matched = 0usize;
let mut end = start;
for (offset, ch) in haystack[start..].char_indices() {
if matched == lowered.len() {
break;
}
let mut consumed_all = true;
for lc in ch.to_lowercase() {
if lowered.get(matched) != Some(&lc) {
consumed_all = false;
break;
}
matched += 1;
}
if !consumed_all {
break;
}
end = start + offset + ch.len_utf8();
}
if matched == lowered.len()
&& !(kind == MatchKind::WholeWord
&& haystack[end..].chars().next().is_some_and(is_word))
{
return Some((start, end));
}
}
None
}
/// Simple whitespace/punctuation tokenizer.
@@ -709,86 +637,4 @@ mod tests {
expanded.iter().map(|x| &x.text).collect::<Vec<_>>()
);
}
#[test]
fn acronyms_only_match_whole_words() {
let ex = QueryExpander::new(QueryExpansionConfig::default());
// "training" contains "ai", "programming" contains "pr". These used to
// be rewritten to "trArtificial Intelligencening" and
// "Pull Requestogramming".
for query in [
"How many miles during my marathon training?",
"Which programming language did I pick?",
"I updated the maintainer list",
] {
for expansion in ex.expand(query) {
assert!(
expansion.expansion_type != "acronym",
"{query:?} produced {expansion:?}"
);
}
}
// A real acronym still expands, in both directions.
let texts: Vec<String> = ex
.expand("What about the API and the database?")
.into_iter()
.filter(|e| e.expansion_type == "acronym")
.map(|e| e.text)
.collect();
assert!(
texts
.iter()
.any(|t| t.contains("Application Programming Interface")),
"{texts:?}"
);
assert!(texts.iter().any(|t| t.contains("DB")), "{texts:?}");
}
#[test]
fn non_ascii_queries_do_not_panic_or_corrupt() {
let ex = QueryExpander::new(QueryExpansionConfig::default());
// Turkish 'İ' is 2 bytes but lowercases to 3, so offsets taken from a
// lowercased copy no longer line up with the original. `"İ AI"` used
// to panic; `"İstanbul AI trip"` used to silently eat a character.
for query in ["İ AI", "İé AI", "İİ ML", "İstanbul AI trip", "ǰ ML notes"] {
for expansion in ex.expand(query) {
assert!(
expansion.text.contains('İ') || expansion.text.contains('ǰ'),
"{query:?} lost its leading character: {expansion:?}"
);
}
}
let expanded = ex.expand("İstanbul AI trip");
assert!(
expanded
.iter()
.any(|e| e.text == "İstanbul Artificial Intelligence trip"),
"{expanded:?}"
);
}
#[test]
fn whole_word_matching_handles_string_edges_and_case() {
assert_eq!(
replace_word_case_insensitive("ai tools", "AI", "Artificial Intelligence"),
"Artificial Intelligence tools"
);
assert_eq!(
replace_word_case_insensitive("tools for ai", "AI", "Artificial Intelligence"),
"tools for Artificial Intelligence"
);
assert_eq!(
replace_word_case_insensitive("the aim", "AI", "Artificial Intelligence"),
"the aim",
"must not match inside a word"
);
assert_eq!(
replace_word_case_insensitive("no match here", "xyz", "abc"),
"no match here"
);
// Only the first occurrence is replaced, as before.
assert_eq!(
replace_word_case_insensitive("ai and ai", "ai", "ML"),
"ML and ai"
);
}
}
crates/clawhdf5-agent/src/reranker.rs
+2 -51
View File
@@ -4,10 +4,8 @@
//! into a single composite score for each retrieved result.
/// Configuration for the multi-factor re-ranker.
#[derive(Debug, Clone, Copy)]
#[derive(Debug, Clone)]
pub struct ReRankConfig {
/// Weight applied to the retrieval score the candidate arrived with.
pub relevance_weight: f32,
/// Weight applied to the temporal decay score (0.0–1.0).
pub temporal_weight: f32,
/// Weight applied to the source authority score (0.0–1.0).
@@ -22,9 +20,6 @@ pub struct ReRankConfig {
impl Default for ReRankConfig {
fn default() -> Self {
Self {
// Relevance leads: the metadata signals break ties and nudge, they
// do not decide. See `BENCHMARKS.md`, "Recency discrimination".
relevance_weight: 1.0,
temporal_weight: 0.3,
authority_weight: 0.2,
activation_weight: 0.5,
@@ -46,8 +41,6 @@ pub struct ReRankResult {
pub authority_score: f32,
/// Normalised Hebbian activation score in [0, 1].
pub activation_score: f32,
/// The retrieval score carried through from the input.
pub relevance_score: f32,
}
/// Compute an exponential decay temporal score.
@@ -112,15 +105,6 @@ pub struct RerankInput {
pub source_channel: String,
/// Raw Hebbian activation weight for this entry.
pub raw_activation: f32,
/// The retrieval score that put this entry in the candidate list.
///
/// Re-ranking is meant to *adjust* the retriever's ordering with signals
/// it does not have, not to replace it. Without this the combined score
/// was made of recency, authority and activation alone, so a candidate
/// pool came back ordered by age with its relevance ordering discarded.
/// Callers with no meaningful score can pass the same value for every
/// entry, which reduces to the old behaviour.
pub relevance: f32,
}
/// Re-rank a list of retrieval results using multi-factor scoring.
@@ -154,8 +138,7 @@ pub fn rerank(
let auth = source_authority_score(&inp.source_channel);
let act = activation_score(inp.raw_activation);
let combined = config.relevance_weight * inp.relevance
+ config.temporal_weight * ts
let combined = config.temporal_weight * ts
+ config.authority_weight * auth
+ config.activation_weight * act;
@@ -165,7 +148,6 @@ pub fn rerank(
temporal_score: ts,
authority_score: auth,
activation_score: act,
relevance_score: inp.relevance,
}
})
.collect();
@@ -271,51 +253,22 @@ mod tests {
timestamp: 0.0, // very old
source_channel: "other".to_string(),
raw_activation: 0.1,
relevance: 0.0,
},
RerankInput {
index: 1,
timestamp: 86_400.0, // one day ago
source_channel: "conversation".to_string(),
raw_activation: 0.5,
relevance: 0.0,
},
RerankInput {
index: 2,
timestamp: 172_800.0, // "now"
source_channel: "user_correction".to_string(),
raw_activation: 1.0,
relevance: 0.0,
},
]
}
#[test]
fn relevance_leads_but_recency_breaks_near_ties() {
let entry = |index, timestamp, relevance| RerankInput {
index,
timestamp,
source_channel: "conversation".to_string(),
raw_activation: 1.0,
relevance,
};
let now = 10.0 * 86_400.0;
let config = ReRankConfig::default();
// A clearly better match wins despite being much older. Before
// `relevance` existed the combined score ignored it entirely, so this
// returned the newer, irrelevant entry.
let ranked = rerank(&[entry(0, 0.0, 1.0), entry(1, now, 0.1)], &config, now);
assert_eq!(ranked[0].index, 0, "{ranked:?}");
// Between near-equal matches, the newer one wins.
let ranked = rerank(&[entry(0, 0.0, 0.80), entry(1, now, 0.79)], &config, now);
assert_eq!(ranked[0].index, 1, "{ranked:?}");
// The breakdown carries the relevance through.
assert_eq!(ranked[0].relevance_score, 0.79);
}
#[test]
fn rerank_returns_all_entries() {
let inputs = make_inputs();
@@ -349,7 +302,6 @@ mod tests {
#[test]
fn rerank_score_breakdown_matches_manual_calculation() {
let config = ReRankConfig {
relevance_weight: 0.0,
temporal_weight: 1.0,
authority_weight: 0.0,
activation_weight: 0.0,
@@ -360,7 +312,6 @@ mod tests {
timestamp: 0.0,
source_channel: "other".to_string(),
raw_activation: 0.5,
relevance: 0.0,
}];
let now = 3600.0_f64; // exactly one half-life later
let results = rerank(&inputs, &config, now);
crates/clawhdf5-agent/src/schema.rs
+38 -493
View File
@@ -12,22 +12,10 @@ use crate::MemoryError;
use crate::cache::MemoryCache;
use crate::knowledge::KnowledgeCache;
use crate::session::SessionCache;
use crate::wal::WalMark;
pub const SCHEMA_VERSION: &str = "1.0";
/// Writer-version tag stored in `/meta` as `edgehdf5_version`. Kept for file
/// compatibility; despite the name it has nothing to do with ZeroClaw, which
/// does not use clawhdf5.
pub const ZEROCLAW_VERSION: &str = "0.8.0";
/// `/meta` attributes holding the [`WalMark`] of the WAL prefix already folded
/// into this file. Absent on files written before the mark existed, and when
/// the checkpoint was taken with an empty WAL.
const WAL_APPLIED_LEN_ATTR: &str = "wal_applied_len";
const WAL_APPLIED_CRC_ATTR: &str = "wal_applied_crc";
const ANN_GENERATION_ATTR: &str = "ann_generation";
const SIG_VERSION_ATTR: &str = "sig_version";
/// Build a complete HDF5 file from the in-memory state.
pub fn build_hdf5_file(
config: &MemoryConfig,
@@ -35,64 +23,6 @@ pub fn build_hdf5_file(
sessions: &SessionCache,
knowledge: &KnowledgeCache,
) -> Result<Vec<u8>, MemoryError> {
build_hdf5_file_with_mark(config, cache, sessions, knowledge, None)
}
/// [`build_hdf5_file`], recording which WAL prefix this state already
/// contains (see [`WalMark`]) so a crash before the WAL is truncated doesn't
/// replay those entries a second time.
pub fn build_hdf5_file_with_mark(
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
wal_applied: Option<WalMark>,
) -> Result<Vec<u8>, MemoryError> {
let meta = CheckpointMeta {
wal_applied,
..CheckpointMeta::default()
};
build_hdf5_file_with_meta(config, cache, sessions, knowledge, &meta)
}
/// Bookkeeping a checkpoint records in `/meta` beside the store's contents.
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct CheckpointMeta {
/// The WAL prefix this checkpoint already contains; see [`WalMark`].
pub wal_applied: Option<WalMark>,
/// Identifies the vector-index sidecar (`<store>.h5.ann`) written with this
/// checkpoint. A sidecar is loaded only if it carries the same value, so
/// one left over from another checkpoint can never be attached to records
/// it wasn't built from.
pub ann_generation: Option<u64>,
/// The checkpoint carries an Ed25519 signature (see [`crate::signing`]).
/// Read-only: whether a checkpoint is *written* signed is decided by the
/// signature passed to [`build_hdf5_file_signed`].
pub signed: bool,
}
/// [`build_hdf5_file`] with checkpoint bookkeeping.
pub fn build_hdf5_file_with_meta(
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
checkpoint: &CheckpointMeta,
) -> Result<Vec<u8>, MemoryError> {
build_hdf5_file_signed(config, cache, sessions, knowledge, checkpoint, None)
}
/// [`build_hdf5_file_with_meta`], plus a signed manifest of the contents
/// (see [`crate::signing`]).
pub fn build_hdf5_file_signed(
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
checkpoint: &CheckpointMeta,
signature: Option<&crate::signing::StoredSignature>,
) -> Result<Vec<u8>, MemoryError> {
let wal_applied = checkpoint.wal_applied;
let mut builder = clawhdf5::FileBuilder::new();
// /meta group with schema attributes
@@ -104,89 +34,15 @@ pub fn build_hdf5_file_signed(
meta.set_attr("embedding_dim", AttrValue::I64(config.embedding_dim as i64));
meta.set_attr("chunk_size", AttrValue::I64(config.chunk_size as i64));
meta.set_attr("overlap", AttrValue::I64(config.overlap as i64));
// Behavioural settings. These used to live only in memory, so reopening a
// store silently reset them to defaults — e.g. a compressed store was
// rewritten uncompressed by the first checkpoint after a reopen. Loaders
// treat each one as optional so older files keep opening.
meta.set_attr("float16", AttrValue::I64(config.float16.into()));
meta.set_attr("compression", AttrValue::I64(config.compression.into()));
meta.set_attr(
"compression_level",
AttrValue::I64(config.compression_level.into()),
);
meta.set_attr(
"compact_threshold",
AttrValue::F64(config.compact_threshold.into()),
);
meta.set_attr("hebbian_boost", AttrValue::F64(config.hebbian_boost.into()));
meta.set_attr("decay_factor", AttrValue::F64(config.decay_factor.into()));
meta.set_attr("wal_enabled", AttrValue::I64(config.wal_enabled.into()));
meta.set_attr(
"wal_max_entries",
AttrValue::I64(config.wal_max_entries as i64),
);
meta.set_attr(
"quantized_index",
AttrValue::I64(config.quantized_index.into()),
);
meta.set_attr("hnsw_m", AttrValue::I64(config.hnsw_m as i64));
meta.set_attr(
"hnsw_ef_construction",
AttrValue::I64(config.hnsw_ef_construction as i64),
);
meta.set_attr(
"hnsw_ef_search",
AttrValue::I64(config.hnsw_ef_search as i64),
);
meta.set_attr(
"edgehdf5_version",
AttrValue::String(ZEROCLAW_VERSION.into()),
);
if let Some(mark) = wal_applied.filter(|m| m.len > 0) {
meta.set_attr(WAL_APPLIED_LEN_ATTR, AttrValue::I64(mark.len as i64));
meta.set_attr(WAL_APPLIED_CRC_ATTR, AttrValue::I64(i64::from(mark.crc)));
}
if let Some(generation) = checkpoint.ann_generation {
// Stored as the i64 with the same bits; attributes have no u64 scalar
// round trip through every reader.
meta.set_attr(ANN_GENERATION_ATTR, AttrValue::I64(generation as i64));
}
if let Some(sig) = signature {
use crate::signing::to_hex;
let m = &sig.manifest;
meta.set_attr(
SIG_VERSION_ATTR,
AttrValue::I64(crate::signing::MANIFEST_VERSION),
);
meta.set_attr("sig_algorithm", AttrValue::String("ed25519".into()));
meta.set_attr("sig_public_key", AttrValue::String(to_hex(&sig.public_key)));
meta.set_attr("sig_signature", AttrValue::String(to_hex(&sig.signature)));
meta.set_attr("sig_record_count", AttrValue::I64(m.record_count as i64));
meta.set_attr(
"sig_records_root",
AttrValue::String(to_hex(&m.records_root)),
);
meta.set_attr("sig_settings", AttrValue::String(to_hex(&m.settings)));
meta.set_attr("sig_sessions", AttrValue::String(to_hex(&m.sessions)));
meta.set_attr("sig_graph", AttrValue::String(to_hex(&m.graph)));
}
// Need at least one dataset in the group for it to be a proper group
meta.create_dataset("_marker").with_u8_data(&[1]).compact();
let finished_meta = meta.finish();
builder.add_group(finished_meta);
// /integrity: the signed per-record hashes, so verification can say
// which records changed.
if let Some(sig) = signature {
let mut group = builder.create_group("integrity");
let flat: Vec<u8> = sig.record_hashes.iter().flatten().copied().collect();
group
.create_dataset("record_hashes")
.with_u8_data(&flat)
.with_shape(&[sig.record_hashes.len() as u64, 32]);
builder.add_group(group.finish());
}
// /memory group
build_memory_group(&mut builder, config, cache)?;
@@ -211,57 +67,32 @@ fn build_memory_group(
// chunks: fixed-length string array
write_string_dataset(&mut group, "chunks", &cache.chunks);
// embeddings: [N x D], f32 — or IEEE half precision for a `float16`
// store. The cache already holds half-rounded values then, so this
// conversion is exact and a reopened store sees the same numbers.
// embeddings: f32 [N x D]
let n = cache.embeddings.len() as u64;
let d = cache.embedding_dim as u64;
let flat = cache.flat_embeddings();
{
let ds = group.create_dataset("embeddings");
let elem_bytes: u64 = if config.float16 {
ds.with_f16_data(flat);
2
} else {
ds.with_f32_data(flat);
4
};
ds.with_shape(&[n, d]);
let ds = group
.create_dataset("embeddings")
.with_f32_data(&flat)
.with_shape(&[n, d]);
// Chunk size tuning: target ~256KB per chunk for optimal I/O
if n > 0 && d > 0 {
let target_chunk_bytes: u64 = 256 * 1024;
let rows_per_chunk = (target_chunk_bytes / (d * elem_bytes)).max(1).min(n);
let rows_per_chunk = (target_chunk_bytes / (d * 4)).max(1).min(n);
ds.with_chunks(&[rows_per_chunk, d]);
// Compression. Shuffle is applied automatically (auto-shuffle
// pre-filter). Zstd is faster than deflate at the same ratio but
// pulls in libzstd, so it is opt-in via the `zstd` feature; the
// default build uses deflate, which is always available. (This
// used to call `with_zstd` unconditionally, so without the
// feature every checkpoint of a compressed store failed with
// "unsupported filter: 32015".) Both are standard HDF5 filters;
// reading a zstd-compressed store needs a zstd-enabled build.
// Compression: Zstd for embeddings — faster than deflate at same ratio.
// Shuffle is applied automatically (auto-shuffle pre-filter).
if config.compression {
#[cfg(feature = "zstd")]
{
let level = if config.compression_level > 0 {
config.compression_level.min(22)
} else {
3 // fast + good ratio for f32 embeddings
3 // Zstd level 3: fast + good ratio for f32 embeddings
};
ds.with_zstd(level);
}
#[cfg(not(feature = "zstd"))]
{
let level = if config.compression_level > 0 {
config.compression_level.min(9)
} else {
4
};
ds.with_deflate(level);
}
}
}
// Skip fill-value initialization — embeddings are fully written
@@ -477,122 +308,17 @@ fn write_string_dataset(
}
}
/// `/meta`'s attributes, failing if any of them cannot be read.
///
/// `Group::attrs` leaves out an attribute it cannot decode. For the store's
/// settings that would silently fall back to defaults (e.g. `float16`, the
/// WAL mark), so an unreadable attribute is an error here, as it was before
/// `attrs` became tolerant.
fn meta_attrs(
file: &clawhdf5::File,
) -> Result<std::collections::HashMap<String, AttrValue>, MemoryError> {
let meta = file
.group("meta")
.map_err(|e| MemoryError::Schema(format!("missing /meta group: {e}")))?;
let (attrs, errors) = meta
.attrs_with_errors()
.map_err(|e| MemoryError::Schema(format!("cannot read /meta attrs: {e}")))?;
if let Some(e) = errors.first() {
return Err(MemoryError::Schema(format!(
"cannot read /meta attrs: {} unreadable, first: {e}",
errors.len()
)));
}
Ok(attrs)
}
/// Validate an HDF5 file has the correct schema and load all data.
/// Read the checkpoint's [`WalMark`] from `/meta`, if it has one.
pub fn read_wal_mark(file: &clawhdf5::File) -> Option<WalMark> {
let attrs = meta_attrs(file).ok()?;
let len = match attrs.get(WAL_APPLIED_LEN_ATTR)? {
AttrValue::I64(v) => u64::try_from(*v).ok()?,
_ => return None,
};
let crc = match attrs.get(WAL_APPLIED_CRC_ATTR)? {
AttrValue::I64(v) => u32::try_from(*v).ok()?,
_ => return None,
};
Some(WalMark { len, crc })
}
/// Read a checkpoint's signature, if it has one. A signature whose
/// attributes are present but malformed is an error, not "unsigned".
pub fn read_signature(
file: &clawhdf5::File,
) -> Result<Option<crate::signing::StoredSignature>, MemoryError> {
use crate::signing::{Manifest, StoredSignature, from_hex};
let attrs = meta_attrs(file)?;
let version = match attrs.get(SIG_VERSION_ATTR) {
None => return Ok(None),
Some(AttrValue::I64(v)) => *v,
Some(_) => return Err(MemoryError::Schema("malformed sig_version".into())),
};
if version != crate::signing::MANIFEST_VERSION {
return Err(MemoryError::Schema(format!(
"unsupported signature version {version}"
)));
}
fn hex<const N: usize>(
attrs: &std::collections::HashMap<String, AttrValue>,
name: &str,
) -> Result<[u8; N], MemoryError> {
match attrs.get(name) {
Some(AttrValue::String(s)) => from_hex::<N>(s),
_ => None,
}
.ok_or_else(|| MemoryError::Schema(format!("malformed or missing {name}")))
}
let record_count = match attrs.get("sig_record_count") {
Some(AttrValue::I64(v)) if *v >= 0 => *v as u64,
_ => return Err(MemoryError::Schema("malformed sig_record_count".into())),
};
let group = file
.group("integrity")
.map_err(|e| MemoryError::Schema(format!("signed checkpoint without /integrity: {e}")))?;
let flat = read_u8_dataset(&group, "record_hashes")?;
if flat.len() % 32 != 0 {
return Err(MemoryError::Schema(
"/integrity/record_hashes is not a whole number of hashes".into(),
));
}
let record_hashes = flat.as_chunks::<32>().0.to_vec();
Ok(Some(StoredSignature {
manifest: Manifest {
record_count,
records_root: hex::<32>(&attrs, "sig_records_root")?,
settings: hex::<32>(&attrs, "sig_settings")?,
sessions: hex::<32>(&attrs, "sig_sessions")?,
graph: hex::<32>(&attrs, "sig_graph")?,
},
record_hashes,
public_key: hex::<32>(&attrs, "sig_public_key")?,
signature: hex::<64>(&attrs, "sig_signature")?,
}))
}
/// Read the checkpoint bookkeeping from `/meta`.
pub fn read_checkpoint_meta(file: &clawhdf5::File) -> CheckpointMeta {
let ann_generation =
meta_attrs(file)
.ok()
.and_then(|attrs| match attrs.get(ANN_GENERATION_ATTR) {
Some(AttrValue::I64(v)) => Some(*v as u64),
_ => None,
});
let signed = meta_attrs(file).is_ok_and(|attrs| attrs.contains_key(SIG_VERSION_ATTR));
CheckpointMeta {
wal_applied: read_wal_mark(file),
ann_generation,
signed,
}
}
pub fn validate_and_load(
file: &clawhdf5::File,
) -> Result<(MemoryConfig, MemoryCache, SessionCache, KnowledgeCache), MemoryError> {
// Read /meta group attributes
let attrs = meta_attrs(file)?;
let meta = file
.group("meta")
.map_err(|e| MemoryError::Schema(format!("missing /meta group: {e}")))?;
let attrs = meta
.attrs()
.map_err(|e| MemoryError::Schema(format!("cannot read /meta attrs: {e}")))?;
let schema_version = match attrs.get("schema_version") {
Some(AttrValue::String(s)) => s.clone(),
@@ -618,45 +344,19 @@ pub fn validate_and_load(
embedding_dim,
chunk_size,
overlap,
float16: optional_bool_attr(&attrs, "float16", false),
compression: optional_bool_attr(&attrs, "compression", false),
compression_level: optional_i64_attr(&attrs, "compression_level")
.and_then(|v| u32::try_from(v).ok())
.unwrap_or(0),
compact_threshold: optional_f32_attr(&attrs, "compact_threshold", 0.3),
hebbian_boost: optional_f32_attr(&attrs, "hebbian_boost", 0.15),
decay_factor: optional_f32_attr(&attrs, "decay_factor", 0.98),
float16: false,
compression: false,
compression_level: 0,
compact_threshold: 0.3,
hebbian_boost: 0.15,
decay_factor: 0.98,
created_at,
wal_enabled: optional_bool_attr(&attrs, "wal_enabled", true),
wal_max_entries: optional_i64_attr(&attrs, "wal_max_entries")
.and_then(|v| usize::try_from(v).ok())
.unwrap_or(500),
// `false`, not the new-store default: a store written before this
// setting existed was built with an f32 index, and reopening it must
// not silently change that.
quantized_index: optional_bool_attr(&attrs, "quantized_index", false),
hnsw_m: optional_i64_attr(&attrs, "hnsw_m")
.and_then(|v| usize::try_from(v).ok())
.unwrap_or(16),
hnsw_ef_construction: optional_i64_attr(&attrs, "hnsw_ef_construction")
.and_then(|v| usize::try_from(v).ok())
.unwrap_or(64),
hnsw_ef_search: optional_i64_attr(&attrs, "hnsw_ef_search")
.and_then(|v| usize::try_from(v).ok())
.unwrap_or(0),
wal_enabled: true,
wal_max_entries: 500,
};
// Load /memory group
let mut memory_cache = load_memory_group(file, embedding_dim)?;
// A float16 store's cache holds half-rounded embeddings. Embeddings read
// from an f16 dataset already are; a float16 store whose last checkpoint
// predates half-precision storage is still f32 on disk and is rounded
// here.
if config.float16 && embeddings_are_f16(file) {
memory_cache.half_precision = true;
} else {
memory_cache.set_half_precision(config.float16);
}
let memory_cache = load_memory_group(file, embedding_dim)?;
// Load /sessions group
let session_cache = load_sessions_group(file)?;
@@ -691,48 +391,27 @@ fn load_memory_group(
let tags = read_string_dataset_from_group(&group, "tags")?;
let tombstones = read_u8_dataset(&group, "tombstones")?;
// Every per-record dataset must describe exactly `n` records. Without
// this, a truncated or hand-edited file loads "successfully" and then
// panics on the first out-of-bounds index during search/delete.
if embedding_dim == 0 {
return Err(MemoryError::Schema(format!(
"/memory has {n} records but embedding_dim is 0"
)));
}
let expected_flat = n.checked_mul(embedding_dim).ok_or_else(|| {
MemoryError::Schema(format!("/memory size overflow: {n} x {embedding_dim}"))
})?;
let check_len = |name: &str, actual: usize, expected: usize| {
if actual == expected {
Ok(())
} else {
Err(MemoryError::Schema(format!(
"/memory/{name} has {actual} entries, expected {expected} \
({n} records)"
)))
}
};
check_len("embeddings", flat_embeddings.len(), expected_flat)?;
check_len("source_channel", source_channels.len(), n)?;
check_len("timestamps", timestamps.len(), n)?;
check_len("session_ids", session_ids.len(), n)?;
check_len("tags", tags.len(), n)?;
check_len("tombstones", tombstones.len(), n)?;
// Norms are derived data: use the stored ones only if they are present
// and the right length, otherwise recompute from the embeddings.
// Read norms if present, otherwise compute from embeddings
let norms = match read_f32_dataset(&group, "norms") {
Ok(stored) if stored.len() == n => stored,
_ => flat_embeddings
Ok(n) if n.len() == n.len() => n,
_ => {
// Compute norms from flat embeddings
flat_embeddings
.chunks(embedding_dim)
.map(|chunk| {
let sq_sum: f32 = chunk.iter().map(|x| x * x).sum();
sq_sum.sqrt()
})
.collect(),
.collect()
}
};
// No unflattening: the cache stores the buffer as it is on disk.
// Unflatten embeddings
let embeddings: Vec<Vec<f32>> = flat_embeddings
.chunks(embedding_dim)
.map(|c| c.to_vec())
.collect();
// Read activation_weights if present, default to vec![1.0; N] for backward compat
let activation_weights = match read_f32_dataset(&group, "activation_weights") {
Ok(w) if w.len() == n => w,
@@ -740,7 +419,7 @@ fn load_memory_group(
};
cache.chunks = chunks;
cache.embeddings.set_flat(embedding_dim, flat_embeddings);
cache.embeddings = embeddings;
cache.source_channels = source_channels;
cache.timestamps = timestamps;
cache.session_ids = session_ids;
@@ -801,7 +480,6 @@ fn load_knowledge_group(file: &clawhdf5::File) -> Result<KnowledgeCache, MemoryE
cache.entities.push(crate::knowledge::Entity {
id: entity_ids[i] as u64,
name: entity_names[i].clone(),
name_lower: entity_names[i].to_lowercase(),
entity_type: entity_types[i].clone(),
embedding_idx: emb_idxs[i],
..Default::default()
@@ -851,27 +529,6 @@ fn extract_string_attr(
}
}
type MetaAttrs = std::collections::HashMap<String, AttrValue>;
fn optional_i64_attr(attrs: &MetaAttrs, name: &str) -> Option<i64> {
match attrs.get(name) {
Some(AttrValue::I64(v)) => Some(*v),
_ => None,
}
}
fn optional_bool_attr(attrs: &MetaAttrs, name: &str, default: bool) -> bool {
optional_i64_attr(attrs, name).map_or(default, |v| v != 0)
}
/// Finite values only: a NaN threshold/decay would poison every comparison.
fn optional_f32_attr(attrs: &MetaAttrs, name: &str, default: f32) -> f32 {
match attrs.get(name) {
Some(AttrValue::F64(v)) if v.is_finite() => *v as f32,
_ => default,
}
}
fn extract_i64_attr(
attrs: &std::collections::HashMap<String, AttrValue>,
name: &str,
@@ -899,13 +556,6 @@ fn read_string_dataset_from_group(
.map_err(|e| MemoryError::Hdf5(format!("cannot read strings from {name}: {e}")))
}
/// Whether `/memory/embeddings` is stored as IEEE half precision.
fn embeddings_are_f16(file: &clawhdf5::File) -> bool {
file.dataset("memory/embeddings")
.and_then(|ds| ds.dtype())
.is_ok_and(|dt| matches!(dt, clawhdf5::DType::Other(ref s) if s == "float16"))
}
fn read_f32_dataset(group: &clawhdf5::Group<'_>, name: &str) -> Result<Vec<f32>, MemoryError> {
let ds = group
.dataset(name)
@@ -964,108 +614,3 @@ fn read_u8_dataset(group: &clawhdf5::Group<'_>, name: &str) -> Result<Vec<u8>, M
.map_err(|e| MemoryError::Hdf5(format!("cannot read u8 from {name}: {e}")))?;
Ok(data.into_iter().map(|v| v as u8).collect())
}
#[cfg(test)]
mod tests {
use super::*;
fn config() -> MemoryConfig {
MemoryConfig::new(std::path::PathBuf::from("unused.h5"), "agent", 4)
}
fn cache_with(n: usize) -> MemoryCache {
let mut cache = MemoryCache::new(4);
for i in 0..n {
cache.push(
format!("chunk {i}"),
vec![i as f32 + 1.0, 0.0, 0.0, 0.0],
"user".into(),
i as f64,
"s".into(),
"t".into(),
);
}
cache
}
fn roundtrip(cache: &MemoryCache) -> Result<MemoryCache, MemoryError> {
let bytes = build_hdf5_file(
&config(),
cache,
&SessionCache::new(),
&KnowledgeCache::new(),
)?;
let file =
clawhdf5::File::from_bytes(bytes).map_err(|e| MemoryError::Hdf5(e.to_string()))?;
validate_and_load(&file).map(|(_, cache, _, _)| cache)
}
#[test]
fn behavioural_config_survives_a_reopen() {
let mut cfg = config();
cfg.compression = true;
cfg.compression_level = 7;
cfg.compact_threshold = 0.5;
cfg.hebbian_boost = 0.25;
cfg.decay_factor = 0.9;
cfg.wal_enabled = false;
cfg.wal_max_entries = 42;
let bytes = build_hdf5_file(
&cfg,
&cache_with(2),
&SessionCache::new(),
&KnowledgeCache::new(),
)
.unwrap();
let file = clawhdf5::File::from_bytes(bytes).unwrap();
let (loaded, loaded_cache, ..) = validate_and_load(&file).unwrap();
// The compressed embeddings must also read back intact.
assert_eq!(loaded_cache.embeddings, cache_with(2).embeddings);
assert!(loaded.compression);
assert_eq!(loaded.compression_level, 7);
assert_eq!(loaded.compact_threshold, 0.5);
assert_eq!(loaded.hebbian_boost, 0.25);
assert_eq!(loaded.decay_factor, 0.9);
assert!(!loaded.wal_enabled);
assert_eq!(loaded.wal_max_entries, 42);
}
#[test]
fn consistent_store_loads() {
let loaded = roundtrip(&cache_with(3)).unwrap();
assert_eq!(loaded.chunks.len(), 3);
assert_eq!(loaded.norms, vec![1.0, 2.0, 3.0]);
}
#[test]
fn wrong_length_norms_are_recomputed_not_trusted() {
// Regression: the guard used to be `n.len() == n.len()`, so a norms
// dataset of any length was accepted and corrupted every cosine score.
let mut cache = cache_with(3);
cache.norms = vec![99.0];
let loaded = roundtrip(&cache).unwrap();
assert_eq!(loaded.norms, vec![1.0, 2.0, 3.0]);
}
#[test]
fn mismatched_per_record_datasets_are_schema_errors() {
type Corrupt = fn(&mut MemoryCache);
let cases: [(&str, Corrupt); 5] = [
("tombstones", |c| c.tombstones.truncate(1)),
("timestamps", |c| c.timestamps.truncate(1)),
("tags", |c| c.tags.truncate(1)),
("session_ids", |c| c.session_ids.truncate(1)),
("source_channel", |c| c.source_channels.truncate(1)),
];
for (name, corrupt) in cases {
let mut cache = cache_with(3);
corrupt(&mut cache);
match roundtrip(&cache) {
Err(MemoryError::Schema(msg)) => {
assert!(msg.contains(name), "{name}: unexpected message {msg}")
}
other => panic!("{name}: expected Schema error, got {:?}", other.map(|_| ())),
}
}
}
}
crates/clawhdf5-agent/src/search.rs
+33 -337
View File
@@ -2,196 +2,59 @@
use std::path::Path;
use std::collections::HashSet;
use crate::bm25;
use crate::confidence::{ConfidenceConfig, ScoredResult, reject_low_confidence};
use crate::hybrid;
use crate::reranker::{ReRankConfig, RerankInput, rerank};
use crate::{HDF5Memory, MAX_ACTIVATION_WEIGHT, MemoryError, Result, SearchResult};
/// Options for [`HDF5Memory::search`].
///
/// [`SearchOptions::new`] is plain hybrid search with the tuned default
/// fusion — the same as `hybrid_search_with(.., hybrid::DEFAULT_FUSION, k)`.
/// Every stage beyond that is opt-in.
#[derive(Debug, Clone)]
pub struct SearchOptions {
/// Number of results to return.
pub k: usize,
/// How the vector and keyword stages are combined.
pub fusion: hybrid::Fusion,
/// Only consider records whose `source_channel` is one of these. The
/// filter applies *before* ranking, so a filtered search still returns up
/// to `k` results and scores are normalised over the records it can
/// return. `None` searches everything; an empty list matches nothing.
pub source_channels: Option<Vec<String>>,
/// Re-rank a candidate pool by retrieval relevance, recency, source
/// authority and activation — the pipeline the OpenClaw backend runs.
pub rerank: Option<ReRankConfig>,
/// Candidates retrieved for re-ranking; 0 means `max(3k, 10)`.
pub rerank_pool: usize,
/// Drop low-confidence results (after re-ranking, when that is on).
pub confidence: Option<ConfidenceConfig>,
/// The time recency is measured from, in seconds since the epoch.
/// `None` uses the system clock.
pub now: Option<f64>,
}
impl SearchOptions {
pub fn new(k: usize) -> Self {
Self {
k,
fusion: hybrid::DEFAULT_FUSION,
source_channels: None,
rerank: None,
rerank_pool: 0,
confidence: None,
now: None,
}
}
pub fn with_fusion(mut self, fusion: hybrid::Fusion) -> Self {
self.fusion = fusion;
self
}
/// Search only records from these source channels.
pub fn with_sources<S: Into<String>>(mut self, channels: impl IntoIterator<Item = S>) -> Self {
self.source_channels = Some(channels.into_iter().map(Into::into).collect());
self
}
pub fn with_rerank(mut self, config: ReRankConfig) -> Self {
self.rerank = Some(config);
self
}
pub fn with_confidence(mut self, config: ConfidenceConfig) -> Self {
self.confidence = Some(config);
self
}
/// Measure recency from `now` (seconds since the epoch) instead of the
/// system clock — for reproducible results and tests.
pub fn at_time(mut self, now: f64) -> Self {
self.now = Some(now);
self
}
}
impl Default for SearchOptions {
fn default() -> Self {
Self::new(10)
}
}
use crate::{HDF5Memory, MemoryError, Result, SearchResult};
impl HDF5Memory {
/// Vector + keyword scoring stage of [`HDF5Memory::search`].
/// Vector + keyword scoring stage of [`HDF5Memory::hybrid_search`].
///
/// Without the `hnsw` feature this is a full linear cosine scan (the exact
/// previous behaviour, also used as the correctness oracle in tests). With
/// `hnsw` enabled and an index available, the vector candidates come from an
/// approximate-nearest-neighbour search over an over-fetched pool, then merge
/// with BM25 via the shared [`hybrid::merge_vector_keyword`].
///
/// `exclude`, when given, marks records that must not be returned (1 =
/// excluded; it covers tombstones too). The index is over-fetched in
/// proportion to how much the mask removes. Surfacing `pool` candidates
/// costs the index roughly `pool × M` distance evaluations, while an exact
/// scan of the allowed records costs one each — so whenever that scan is
/// the cheaper of the two it is used instead, and it is also the fallback
/// if the pool comes back with too few allowed hits (the allowed records
/// sit away from the query). A filtered search never comes back short.
#[cfg(feature = "hnsw")]
fn vector_keyword_search(
&mut self,
query_embedding: &[f32],
query_text: &str,
bm25: &bm25::BM25Index,
fusion: hybrid::Fusion,
vector_weight: f32,
keyword_weight: f32,
k: usize,
exclude: Option<&[u8]>,
) -> Vec<(usize, f32)> {
self.ensure_hnsw_fresh();
let n = self.cache.len();
// Over-fetch so the merge sees a useful vector pool. `ef` is
// configurable, but the pool the fusion stage sees is not tied to it:
// a caller lowering `ef` for speed should not silently narrow what
// fusion has to work with.
let mut pool = (k * 8).max(64);
let mut allowed = n;
if let Some(ex) = exclude {
allowed = ex.iter().filter(|&&e| e == 0).count();
if allowed == 0 {
return Vec::new();
}
// Expect `pool` allowed hits if the filter is independent of the
// query's neighbourhood.
pool = pool.saturating_mul(n).div_ceil(allowed);
if allowed <= pool.saturating_mul(self.hnsw_m()) {
return self.exact_masked_search(query_embedding, query_text, bm25, fusion, k, ex);
}
}
match self.hnsw.as_ref() {
Some(index) if !index.is_empty() && index.dimension() == query_embedding.len() => {
let ef = self.hnsw_ef_search(k).max(pool);
let candidates = index.search(query_embedding, pool, ef);
// A quantised index returns approximate distances, and no
// amount of `ef` fixes that — the loss is in the distances,
// not the graph. Re-score the pool against the cache's exact
// embeddings, which cost nothing extra to keep: recall then
// matches an f32 index. See `BENCHMARKS.md`.
let exact = index.storage() == clawhdf5_ann::Storage::Int8;
let vec_scores: Vec<(usize, f32)> = candidates
// Over-fetch so the merge sees a useful vector pool; cosine
// distance from the index converts back to similarity (1 - d).
let pool = (k * 8).max(64);
let vec_scores: Vec<(usize, f32)> = index
.search(query_embedding, pool, pool)
.into_iter()
.filter(|(id, _)| exclude.is_none_or(|ex| ex[*id] == 0))
.map(|(id, dist)| {
let score = if exact {
crate::vector_search::cosine_similarity(
query_embedding,
&self.cache.embeddings[id],
)
} else {
1.0 - dist
};
(id, score)
})
.map(|(id, dist)| (id, 1.0 - dist))
.collect();
// Fusion normalises over every keyword match, so it needs all
// the scores — but not ranked.
let mut kw_scores = bm25.scores(query_text);
if let Some(ex) = exclude {
if vec_scores.len() < k.min(allowed) {
// The allowed records are not where the index looked.
return self.exact_masked_search(
query_embedding,
query_text,
bm25,
fusion,
let kw_scores = bm25.search(query_text, self.cache.len());
hybrid::merge_vector_keyword(
vec_scores,
kw_scores,
vector_weight,
keyword_weight,
k,
ex,
);
)
}
kw_scores.retain(|(id, _)| ex[*id] == 0);
}
hybrid::fuse(vec_scores, kw_scores, fusion, k)
}
_ => match exclude {
Some(ex) => {
self.exact_masked_search(query_embedding, query_text, bm25, fusion, k, ex)
}
None => hybrid::hybrid_search_fused(
_ => hybrid::hybrid_search(
query_embedding,
query_text,
&self.cache.embeddings,
&self.cache.chunks,
&self.cache.tombstones,
bm25,
fusion,
vector_weight,
keyword_weight,
k,
),
},
}
}
@@ -201,52 +64,21 @@ impl HDF5Memory {
query_embedding: &[f32],
query_text: &str,
bm25: &bm25::BM25Index,
fusion: hybrid::Fusion,
vector_weight: f32,
keyword_weight: f32,
k: usize,
exclude: Option<&[u8]>,
) -> Vec<(usize, f32)> {
match exclude {
Some(ex) => self.exact_masked_search(query_embedding, query_text, bm25, fusion, k, ex),
None => hybrid::hybrid_search_fused(
hybrid::hybrid_search(
query_embedding,
query_text,
&self.cache.embeddings,
&self.cache.chunks,
&self.cache.tombstones,
bm25,
fusion,
vector_weight,
keyword_weight,
k,
),
}
}
/// Exact hybrid search over the records `exclude` leaves (0 = allowed).
fn exact_masked_search(
&self,
query_embedding: &[f32],
query_text: &str,
bm25: &bm25::BM25Index,
fusion: hybrid::Fusion,
k: usize,
exclude: &[u8],
) -> Vec<(usize, f32)> {
let vec_scores =
hybrid::exact_vector_scores(query_embedding, &self.cache.embeddings, exclude);
let mut kw_scores = bm25.scores(query_text);
kw_scores.retain(|(id, _)| exclude.get(*id) == Some(&0));
hybrid::fuse(vec_scores, kw_scores, fusion, k)
}
/// The exclusion mask for a source-channel filter: 1 for a tombstoned
/// record or one from a channel not in `channels`.
fn source_mask(&self, channels: &[String]) -> Vec<u8> {
let allowed: HashSet<&str> = channels.iter().map(String::as_str).collect();
self.cache
.source_channels
.iter()
.zip(&self.cache.tombstones)
.map(|(ch, &t)| u8::from(t != 0 || !allowed.contains(ch.as_str())))
.collect()
)
}
/// Perform hybrid search combining cosine vector similarity and BM25 keyword search.
@@ -258,76 +90,15 @@ impl HDF5Memory {
keyword_weight: f32,
k: usize,
) -> Vec<SearchResult> {
self.hybrid_search_with(
query_embedding,
query_text,
hybrid::Fusion::Weighted {
vector: vector_weight,
keyword: keyword_weight,
},
k,
)
}
/// [`HDF5Memory::hybrid_search`] with the fusion method chosen explicitly.
///
/// [`hybrid::DEFAULT_FUSION`] is what the weighted form defaults to;
/// [`hybrid::Fusion::Rrf`] combines the two stages by rank instead of by
/// score.
pub fn hybrid_search_with(
&mut self,
query_embedding: &[f32],
query_text: &str,
fusion: hybrid::Fusion,
k: usize,
) -> Vec<SearchResult> {
self.search(
query_embedding,
query_text,
&SearchOptions::new(k).with_fusion(fusion),
)
}
/// Hybrid search with optional source filtering, re-ranking and
/// confidence rejection — see [`SearchOptions`].
///
/// Stages, in order: vector + keyword retrieval over the records the
/// source filter allows; fusion; scaling by Hebbian activation; re-ranking
/// (if on) of a `rerank_pool` of candidates; confidence rejection (if on);
/// the top `k`. The records returned with a positive score get their
/// Hebbian boost.
pub fn search(
&mut self,
query_embedding: &[f32],
query_text: &str,
options: &SearchOptions,
) -> Vec<SearchResult> {
let k = options.k;
let fetch = match options.rerank {
Some(_) if options.rerank_pool > 0 => options.rerank_pool.max(k),
Some(_) => k.saturating_mul(3).max(10),
None => k,
};
let exclude = options
.source_channels
.as_deref()
.map(|channels| self.source_mask(channels));
// The keyword index lives for the life of the store and is updated
// incrementally. Take it out for the duration of the call so the
// vector stage can borrow `self` mutably, then put it back.
self.ensure_bm25_fresh();
let bm25 = self.bm25.take().expect("ensure_bm25_fresh leaves an index");
let bm25 = bm25::BM25Index::build(&self.cache.chunks, &self.cache.tombstones);
let scored = self.vector_keyword_search(
query_embedding,
query_text,
&bm25,
options.fusion,
fetch,
exclude.as_deref(),
vector_weight,
keyword_weight,
k,
);
self.bm25 = Some(bm25);
let mut results: Vec<SearchResult> = scored
.into_iter()
.map(|(idx, score)| {
@@ -342,98 +113,23 @@ impl HDF5Memory {
}
})
.collect();
// Ties broken by index so results (and therefore which records get
// boosted) don't depend on HashMap iteration order upstream.
results.sort_by(|a, b| {
b.score
.partial_cmp(&a.score)
.unwrap_or(std::cmp::Ordering::Equal)
.then(a.index.cmp(&b.index))
});
if let Some(config) = &options.rerank {
results = Self::rerank_results(results, config, options.now);
}
if let Some(config) = &options.confidence {
let scored: Vec<ScoredResult> = results
.iter()
.map(|r| ScoredResult {
index: r.index,
score: r.score,
})
.collect();
let keep: HashSet<usize> = reject_low_confidence(&scored, config)
.into_iter()
.map(|r| r.index)
.collect();
results.retain(|r| keep.contains(&r.index));
}
results.truncate(k);
// Only reinforce records that actually matched. When fewer than `k`
// records are relevant, the rest of the list is zero-score filler;
// boosting it would teach the store that arbitrary records are
// important just because they were nearby in iteration order.
let hit_indices: Vec<usize> = results
.iter()
.filter(|r| r.score > 0.0)
.map(|r| r.index)
.collect();
let hit_indices: Vec<usize> = results.iter().map(|r| r.index).collect();
self.apply_hebbian_boost(&hit_indices);
self.flush().ok();
results
}
/// Reorder by the re-ranker's combined score, which also becomes each
/// result's `score`.
fn rerank_results(
results: Vec<SearchResult>,
config: &ReRankConfig,
now: Option<f64>,
) -> Vec<SearchResult> {
let now = now.unwrap_or_else(|| {
std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.map(|d| d.as_secs_f64())
.unwrap_or(0.0)
});
let inputs: Vec<RerankInput> = results
.iter()
.map(|r| RerankInput {
index: r.index,
timestamp: r.timestamp,
source_channel: r.source_channel.clone(),
raw_activation: r.activation,
relevance: r.score,
})
.collect();
let mut by_index: std::collections::HashMap<usize, SearchResult> =
results.into_iter().map(|r| (r.index, r)).collect();
rerank(&inputs, config, now)
.into_iter()
.filter_map(|rr| {
let mut r = by_index.remove(&rr.index)?;
r.score = rr.combined_score;
Some(r)
})
.collect()
}
/// Reinforce the records a query returned. The new weights are persisted by
/// the next checkpoint (any write that flushes, `flush_wal`, or drop) — not
/// by rewriting the whole store inside the query, which is what made
/// `hybrid_search` cost O(store size) in disk I/O. They are a ranking hint,
/// not user data: a crash before the next checkpoint only forgets the
/// boosts since the last one.
fn apply_hebbian_boost(&mut self, hit_indices: &[usize]) {
if hit_indices.is_empty() || self.config.hebbian_boost == 0.0 {
return;
}
for &idx in hit_indices {
let w = &mut self.cache.activation_weights[idx];
*w = (*w + self.config.hebbian_boost).min(MAX_ACTIVATION_WEIGHT);
self.cache.activation_weights[idx] += self.config.hebbian_boost;
}
self.activations_dirty = true;
}
/// Get the chunk text for a memory entry by index.
crates/clawhdf5-agent/src/session.rs
+1 -16
View File
@@ -33,7 +33,7 @@ impl SessionCache {
self.entries.is_empty()
}
/// Add a new session with its summary, timestamped now.
/// Add a new session with its summary.
pub fn add(
&mut self,
id: &str,
@@ -47,21 +47,6 @@ impl SessionCache {
.unwrap_or_default()
.as_secs_f64()
* 1_000_000.0; // microseconds
self.add_at(id, start_idx, end_idx, channel, summary, ts);
}
/// Add a session with an explicit timestamp (Unix **microseconds**, the
/// unit [`SessionEntry::ts`] uses) — for importers carrying sessions over
/// from another store, whose original time should be kept.
pub fn add_at(
&mut self,
id: &str,
start_idx: usize,
end_idx: usize,
channel: &str,
summary: &str,
ts: f64,
) {
self.entries.push(SessionEntry {
id: id.to_string(),
start_idx: start_idx as u64,
crates/clawhdf5-agent/src/signing.rs
-419
View File
@@ -1,419 +0,0 @@
//! Ed25519-signed checkpoints.
//!
//! When a signing key is set ([`crate::HDF5Memory::set_signing_key`]), every
//! checkpoint writes a signed manifest of the store: a SHA-256 per memory
//! record rolled into a Merkle root, plus hashes of the store's settings, its
//! sessions and its knowledge graph. [`verify_store`] recomputes all of it from
//! the file and checks the signature against a public key the caller trusts,
//! so any change to the checkpointed file — a record's text or embedding, a
//! setting, a session, a graph edge, made through this crate or any other HDF5
//! tool — is detected, and the per-record hashes say which records changed.
//!
//! What it does not cover: saves still only in the WAL (made since the last
//! checkpoint). [`VerifyReport::wal_entries_unsigned`] counts them.
//!
//! The hashes cover exactly what the file persists, in the form the loader
//! returns it, so a store verifies after any number of reopen/checkpoint
//! cycles. Derived data (L2 norms, the vector index) is not covered; it is
//! recomputed from covered data.
use ed25519_dalek::{Signature, Signer, Verifier};
pub use ed25519_dalek::{SigningKey, VerifyingKey};
use sha2::{Digest, Sha256};
use crate::MemoryConfig;
use crate::cache::MemoryCache;
use crate::knowledge::KnowledgeCache;
use crate::session::SessionCache;
use crate::wal::WalMark;
/// Version of the manifest encoding; part of what is signed.
pub const MANIFEST_VERSION: i64 = 1;
type Hash = [u8; 32];
/// The hashes a signature covers.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct Manifest {
pub record_count: u64,
/// Merkle root over the per-record hashes.
pub records_root: Hash,
/// Settings persisted in `/meta`, plus the checkpoint's WAL mark.
pub settings: Hash,
pub sessions: Hash,
pub graph: Hash,
}
impl Manifest {
/// The exact bytes that are signed.
pub fn signed_bytes(&self) -> Vec<u8> {
let mut m = Vec::with_capacity(160);
m.extend_from_slice(b"clawhdf5-agent signed checkpoint\0");
m.extend_from_slice(&MANIFEST_VERSION.to_le_bytes());
m.extend_from_slice(&self.record_count.to_le_bytes());
m.extend_from_slice(&self.records_root);
m.extend_from_slice(&self.settings);
m.extend_from_slice(&self.sessions);
m.extend_from_slice(&self.graph);
m
}
}
/// A signature as stored in a checkpoint.
#[derive(Debug, Clone)]
pub struct StoredSignature {
pub manifest: Manifest,
pub record_hashes: Vec<Hash>,
pub public_key: [u8; 32],
pub signature: [u8; 64],
}
/// Build the manifest (and per-record hashes) for the state about to be
/// checkpointed, and sign it.
pub fn sign(
key: &SigningKey,
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
wal_applied: Option<WalMark>,
) -> StoredSignature {
let (manifest, record_hashes) = manifest(config, cache, sessions, knowledge, wal_applied);
let signature = key.sign(&manifest.signed_bytes()).to_bytes();
StoredSignature {
manifest,
record_hashes,
public_key: key.verifying_key().to_bytes(),
signature,
}
}
/// Compute the manifest of a store's state.
pub fn manifest(
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
wal_applied: Option<WalMark>,
) -> (Manifest, Vec<Hash>) {
let record_hashes: Vec<Hash> = (0..cache.len()).map(|i| record_hash(cache, i)).collect();
let manifest = Manifest {
record_count: cache.len() as u64,
records_root: merkle_root(&record_hashes),
settings: settings_hash(config, wal_applied),
sessions: sessions_hash(sessions),
graph: graph_hash(knowledge),
};
(manifest, record_hashes)
}
// ---------------------------------------------------------------------------
// Canonical encoding
// ---------------------------------------------------------------------------
/// A SHA-256 over length-prefixed fields, so no two different field lists
/// hash the same bytes.
struct Fields(Sha256);
impl Fields {
fn new(domain: &str) -> Self {
let mut h = Sha256::new();
h.update((domain.len() as u64).to_le_bytes());
h.update(domain.as_bytes());
Self(h)
}
fn bytes(&mut self, b: &[u8]) -> &mut Self {
self.0.update((b.len() as u64).to_le_bytes());
self.0.update(b);
self
}
/// Strings as the loader returns them: stored null-padded, so a trailing
/// NUL cannot survive a round trip and must not be part of the hash.
fn str(&mut self, s: &str) -> &mut Self {
self.bytes(s.trim_end_matches('\0').as_bytes())
}
fn u64(&mut self, v: u64) -> &mut Self {
self.0.update(v.to_le_bytes());
self
}
fn f64(&mut self, v: f64) -> &mut Self {
self.0.update(v.to_bits().to_le_bytes());
self
}
fn f32(&mut self, v: f32) -> &mut Self {
self.0.update(v.to_bits().to_le_bytes());
self
}
fn finish(self) -> Hash {
self.0.finalize().into()
}
}
/// Everything persisted about record `i`, including its position. The
/// embedding is hashed as the cache holds it — for a `float16` store that is
/// the half-rounded value the file holds.
fn record_hash(cache: &MemoryCache, i: usize) -> Hash {
let mut f = Fields::new("clawhdf5-agent/record");
f.u64(i as u64).str(&cache.chunks[i]);
let emb: Vec<u8> = cache.embeddings[i]
.iter()
.flat_map(|v| v.to_bits().to_le_bytes())
.collect();
f.bytes(&emb)
.str(&cache.source_channels[i])
.f64(cache.timestamps[i])
.str(&cache.session_ids[i])
.str(&cache.tags[i])
.u64(u64::from(cache.tombstones[i]))
.f32(cache.activation_weights[i]);
f.finish()
}
/// Binary Merkle tree: leaves are the record hashes; a parent hashes its two
/// children with a node prefix; an odd node is carried up unchanged.
fn merkle_root(leaves: &[Hash]) -> Hash {
if leaves.is_empty() {
return Fields::new("clawhdf5-agent/merkle-empty").finish();
}
let mut level: Vec<Hash> = leaves.to_vec();
while level.len() > 1 {
level = level
.chunks(2)
.map(|pair| match pair {
[l, r] => {
let mut h = Sha256::new();
h.update([1u8]);
h.update(l);
h.update(r);
h.finalize().into()
}
[only] => *only,
_ => unreachable!(),
})
.collect();
}
level[0]
}
fn settings_hash(c: &MemoryConfig, wal_applied: Option<WalMark>) -> Hash {
let mut f = Fields::new("clawhdf5-agent/settings");
f.str(crate::schema::SCHEMA_VERSION)
.str(&c.created_at)
.str(&c.agent_id)
.str(&c.embedder)
.u64(c.embedding_dim as u64)
.u64(c.chunk_size as u64)
.u64(c.overlap as u64)
.u64(u64::from(c.float16))
.u64(u64::from(c.compression))
.u64(u64::from(c.compression_level))
.f32(c.compact_threshold)
.f32(c.hebbian_boost)
.f32(c.decay_factor)
.u64(u64::from(c.wal_enabled))
.u64(c.wal_max_entries as u64)
.u64(u64::from(c.quantized_index))
.u64(c.hnsw_m as u64)
.u64(c.hnsw_ef_construction as u64)
.u64(c.hnsw_ef_search as u64);
// An empty mark is not written to the file, so it must hash as none.
match wal_applied.filter(|m| m.len > 0) {
Some(m) => f.u64(1).u64(m.len).u64(u64::from(m.crc)),
None => f.u64(0),
};
f.finish()
}
fn sessions_hash(s: &SessionCache) -> Hash {
let mut f = Fields::new("clawhdf5-agent/sessions");
f.u64(s.entries.len() as u64);
for (i, e) in s.entries.iter().enumerate() {
f.str(&e.id)
.u64(e.start_idx)
.u64(e.end_idx)
.str(&e.channel)
.f64(e.ts)
.str(s.summaries.get(i).map(String::as_str).unwrap_or(""));
}
f.finish()
}
fn graph_hash(k: &KnowledgeCache) -> Hash {
let mut f = Fields::new("clawhdf5-agent/graph");
f.u64(k.entities.len() as u64);
for e in &k.entities {
f.u64(e.id)
.str(&e.name)
.str(&e.entity_type)
.u64(e.embedding_idx as u64);
}
f.u64(k.relations.len() as u64);
for r in &k.relations {
f.u64(r.src)
.u64(r.tgt)
.str(&r.relation)
.f32(r.weight)
.f64(r.ts);
}
f.u64(k.alias_strings.len() as u64);
for (s, id) in k.alias_strings.iter().zip(&k.alias_entity_ids) {
f.str(s).u64(*id as u64);
}
f.finish()
}
// ---------------------------------------------------------------------------
// Verification
// ---------------------------------------------------------------------------
/// The outcome of [`verify_store`].
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct VerifyReport {
/// The checkpoint carries a signature.
pub signed: bool,
/// The signature was made by the key the caller trusts.
pub key_matches: bool,
/// The signature over the stored manifest is valid.
pub signature_valid: bool,
/// The file's current contents match the signed manifest.
pub records_match: bool,
pub settings_match: bool,
pub sessions_match: bool,
pub graph_match: bool,
/// Records whose contents differ from what was signed (by position),
/// when the stored per-record hashes are themselves authentic.
pub changed_records: Vec<usize>,
/// Records in the file versus in the signed manifest.
pub record_count: u64,
pub signed_record_count: u64,
/// The public key the checkpoint claims to be signed by.
pub public_key: Option<[u8; 32]>,
/// Saves in the WAL after the checkpoint: not covered by the signature.
pub wal_entries_unsigned: usize,
}
impl VerifyReport {
/// Signed by the trusted key, signature valid, and every part of the
/// file unchanged since it was signed.
pub fn is_valid(&self) -> bool {
self.signed
&& self.key_matches
&& self.signature_valid
&& self.records_match
&& self.settings_match
&& self.sessions_match
&& self.graph_match
}
}
/// Check a store file against the public key the caller trusts.
///
/// Reads the checkpoint (not the WAL), recomputes every hash from its
/// contents and checks the signature. Never writes.
pub fn verify_store(
path: &std::path::Path,
trusted: &VerifyingKey,
) -> Result<VerifyReport, crate::MemoryError> {
let file = clawhdf5::File::open(path)
.map_err(|e| crate::MemoryError::Hdf5(format!("cannot open {}: {e}", path.display())))?;
let (config, cache, sessions, knowledge) = crate::schema::validate_and_load(&file)?;
let checkpoint = crate::schema::read_checkpoint_meta(&file);
let stored = crate::schema::read_signature(&file)?;
let wal_entries_unsigned = count_wal_entries_after(path, checkpoint.wal_applied);
let (current, current_hashes) = manifest(
&config,
&cache,
&sessions,
&knowledge,
checkpoint.wal_applied,
);
let Some(stored) = stored else {
return Ok(VerifyReport {
signed: false,
key_matches: false,
signature_valid: false,
records_match: false,
settings_match: false,
sessions_match: false,
graph_match: false,
changed_records: Vec::new(),
record_count: current.record_count,
signed_record_count: 0,
public_key: None,
wal_entries_unsigned,
});
};
let key_matches = stored.public_key == trusted.to_bytes();
let signature_valid = trusted
.verify(
&stored.manifest.signed_bytes(),
&Signature::from_bytes(&stored.signature),
)
.is_ok();
// The stored per-record hashes can localise a change only if they are
// the ones that were signed.
let hashes_authentic = signature_valid
&& stored.record_hashes.len() as u64 == stored.manifest.record_count
&& merkle_root(&stored.record_hashes) == stored.manifest.records_root;
let changed_records = if hashes_authentic {
let n = current_hashes.len().max(stored.record_hashes.len());
(0..n)
.filter(|&i| current_hashes.get(i) != stored.record_hashes.get(i))
.collect()
} else {
Vec::new()
};
Ok(VerifyReport {
signed: true,
key_matches,
signature_valid,
records_match: signature_valid
&& current.record_count == stored.manifest.record_count
&& current.records_root == stored.manifest.records_root,
settings_match: signature_valid && current.settings == stored.manifest.settings,
sessions_match: signature_valid && current.sessions == stored.manifest.sessions,
graph_match: signature_valid && current.graph == stored.manifest.graph,
changed_records,
record_count: current.record_count,
signed_record_count: stored.manifest.record_count,
public_key: Some(stored.public_key),
wal_entries_unsigned,
})
}
fn count_wal_entries_after(store: &std::path::Path, mark: Option<WalMark>) -> usize {
let wal = store.with_extension("h5.wal");
if !wal.exists() {
return 0;
}
crate::wal::WalFile::read_entries_for_migration(&wal, mark)
.map(|e| e.len())
.unwrap_or(0)
}
/// A new random signing key from the operating system's RNG.
pub fn generate_key() -> SigningKey {
SigningKey::generate(&mut rand_core::OsRng)
}
/// Hex encoding for keys and signatures in attributes and the CLI.
pub fn to_hex(bytes: &[u8]) -> String {
bytes.iter().map(|b| format!("{b:02x}")).collect()
}
/// Parse hex into exactly `N` bytes.
pub fn from_hex<const N: usize>(s: &str) -> Option<[u8; N]> {
let s = s.trim();
if s.len() != 2 * N {
return None;
}
let mut out = [0u8; N];
for (i, byte) in out.iter_mut().enumerate() {
*byte = u8::from_str_radix(&s[2 * i..2 * i + 2], 16).ok()?;
}
Some(out)
}
crates/clawhdf5-agent/src/storage.rs
+10 -109
View File
@@ -11,7 +11,6 @@ use crate::cache::MemoryCache;
use crate::knowledge::KnowledgeCache;
use crate::schema;
use crate::session::SessionCache;
use crate::wal::WalMark;
/// Write all in-memory state to an HDF5 file on disk.
pub fn write_to_disk(
@@ -21,50 +20,7 @@ pub fn write_to_disk(
sessions: &SessionCache,
knowledge: &KnowledgeCache,
) -> Result<(), MemoryError> {
write_to_disk_with_mark(path, config, cache, sessions, knowledge, None)
}
/// [`write_to_disk`] for a checkpoint: `wal_applied` is the mark of the WAL
/// prefix whose entries `cache` already contains.
pub fn write_to_disk_with_mark(
path: &Path,
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
wal_applied: Option<WalMark>,
) -> Result<(), MemoryError> {
let meta = schema::CheckpointMeta {
wal_applied,
..schema::CheckpointMeta::default()
};
write_to_disk_with_meta(path, config, cache, sessions, knowledge, &meta)
}
/// [`write_to_disk`] with full checkpoint bookkeeping.
pub fn write_to_disk_with_meta(
path: &Path,
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
checkpoint: &schema::CheckpointMeta,
) -> Result<(), MemoryError> {
write_to_disk_signed(path, config, cache, sessions, knowledge, checkpoint, None)
}
/// [`write_to_disk_with_meta`] with a signed manifest of the contents.
pub fn write_to_disk_signed(
path: &Path,
config: &MemoryConfig,
cache: &MemoryCache,
sessions: &SessionCache,
knowledge: &KnowledgeCache,
checkpoint: &schema::CheckpointMeta,
signature: Option<&crate::signing::StoredSignature>,
) -> Result<(), MemoryError> {
let bytes =
schema::build_hdf5_file_signed(config, cache, sessions, knowledge, checkpoint, signature)?;
let bytes = schema::build_hdf5_file(config, cache, sessions, knowledge)?;
if bytes.is_empty() {
return Err(MemoryError::Hdf5("build_hdf5_file produced 0 bytes".into()));
@@ -72,41 +28,9 @@ pub fn write_to_disk_signed(
// Write to a temp file first, then rename for atomicity
let tmp_path = path.with_extension("h5.tmp");
write_synced(&tmp_path, &bytes)?;
rename_synced(&tmp_path, path)
}
std::fs::write(&tmp_path, &bytes).map_err(MemoryError::Io)?;
std::fs::rename(&tmp_path, path).map_err(MemoryError::Io)?;
/// Write `bytes` to `path` and flush them to stable storage.
pub(crate) fn write_synced(path: &Path, bytes: &[u8]) -> Result<(), MemoryError> {
use std::io::Write;
let mut f = std::fs::File::create(path).map_err(MemoryError::Io)?;
f.write_all(bytes).map_err(MemoryError::Io)?;
f.sync_all().map_err(MemoryError::Io)
}
/// Rename `from` over `to`, then sync the parent directory so the rename
/// itself survives a power loss. `from` must already be synced: without that,
/// the rename can reach disk before the data and leave an empty or partial
/// file under the final name.
///
/// This is per-checkpoint/snapshot cost only (each is already a full file
/// write). Individual WAL appends are deliberately not synced — see the
/// durability notes in the crate docs.
pub(crate) fn rename_synced(from: &Path, to: &Path) -> Result<(), MemoryError> {
std::fs::rename(from, to).map_err(MemoryError::Io)?;
#[cfg(unix)]
if let Some(dir) = to.parent() {
let dir = if dir.as_os_str().is_empty() {
Path::new(".")
} else {
dir
};
// Directory fsync is best-effort: some filesystems refuse it, and the
// rename has already happened.
if let Ok(d) = std::fs::File::open(dir) {
let _ = d.sync_all();
}
}
Ok(())
}
@@ -118,39 +42,19 @@ pub(crate) fn rename_synced(from: &Path, to: &Path) -> Result<(), MemoryError> {
pub fn read_from_disk(
path: &Path,
) -> Result<(MemoryConfig, MemoryCache, SessionCache, KnowledgeCache), MemoryError> {
read_from_disk_with_mark(path).map(|(state, _mark)| state)
}
let mmap = clawhdf5_io::MmapReader::open(path).map_err(MemoryError::Io)?;
/// Everything [`read_from_disk`] returns.
pub type StoreState = (MemoryConfig, MemoryCache, SessionCache, KnowledgeCache);
// Advise the OS we'll need the whole file for parsing
mmap.advise_willneed(0, mmap.len());
/// [`read_from_disk`], plus the checkpoint's [`WalMark`] (if any) so the
/// caller can skip WAL entries this file already contains.
pub fn read_from_disk_with_mark(path: &Path) -> Result<(StoreState, Option<WalMark>), MemoryError> {
// `File::open` memory-maps the file itself (the facade's `mmap` feature is
// on by default). Mapping it here and handing over `as_bytes().to_vec()`
// did the same work and then copied the whole store — a second full copy
// of the file, live for the whole parse, on top of the mapping.
let file = clawhdf5::File::open(path)
// Parse the HDF5 file from the mmap'd bytes
let file = clawhdf5::File::from_bytes(mmap.as_bytes().to_vec())
.map_err(|e| MemoryError::Hdf5(format!("cannot open {}: {e}", path.display())))?;
let (mut config, cache, sessions, knowledge) = schema::validate_and_load(&file)?;
config.path = path.to_path_buf();
let wal_applied = schema::read_wal_mark(&file);
Ok(((config, cache, sessions, knowledge), wal_applied))
}
/// [`read_from_disk`], plus all checkpoint bookkeeping.
pub fn read_from_disk_with_meta(
path: &Path,
) -> Result<(StoreState, schema::CheckpointMeta), MemoryError> {
let file = clawhdf5::File::open(path)
.map_err(|e| MemoryError::Hdf5(format!("cannot open {}: {e}", path.display())))?;
let (mut config, cache, sessions, knowledge) = schema::validate_and_load(&file)?;
config.path = path.to_path_buf();
let meta = schema::read_checkpoint_meta(&file);
Ok(((config, cache, sessions, knowledge), meta))
Ok((config, cache, sessions, knowledge))
}
/// Copy an HDF5 file atomically to a destination.
@@ -174,10 +78,7 @@ pub fn snapshot_file(src: &Path, dest: &Path) -> Result<std::path::PathBuf, Memo
// Atomic copy: write to temp, then rename
let tmp_path = dest_file.with_extension("h5.tmp");
std::fs::copy(src, &tmp_path).map_err(MemoryError::Io)?;
std::fs::File::open(&tmp_path)
.and_then(|f| f.sync_all())
.map_err(MemoryError::Io)?;
rename_synced(&tmp_path, &dest_file)?;
std::fs::rename(&tmp_path, &dest_file).map_err(MemoryError::Io)?;
Ok(dest_file)
}
crates/clawhdf5-agent/src/store_lock.rs
-79
View File
@@ -1,79 +0,0 @@
//! Single-writer guard for a memory store.
//!
//! `HDF5Memory` keeps the whole store in memory and rewrites the `.h5` file at
//! every checkpoint, so two handles on one store (two processes, or two opens
//! in one process) silently destroy each other's data: whoever checkpoints
//! last wins, and both append to the same WAL with independent CRC chains.
//! The lock turns that into an immediate, explicit error.
use std::fs::{File, OpenOptions, TryLockError};
use std::path::{Path, PathBuf};
use crate::MemoryError;
const LOCK_RETRIES: u32 = 25;
const LOCK_RETRY_DELAY: std::time::Duration = std::time::Duration::from_millis(10);
/// An exclusive advisory lock on `<store>.h5.lock`, held for the lifetime of
/// the owning `HDF5Memory` and released when it is dropped (or when the
/// process dies — the OS drops the lock with the file descriptor, so a crash
/// never leaves a stale lock behind; the empty lock file itself is harmless).
#[derive(Debug)]
pub(crate) struct StoreLock {
_file: File,
}
impl StoreLock {
pub(crate) fn lock_path(store: &Path) -> PathBuf {
store.with_extension("h5.lock")
}
pub(crate) fn acquire(store: &Path) -> Result<Self, MemoryError> {
let path = Self::lock_path(store);
let file = OpenOptions::new()
.create(true)
.truncate(false)
.write(true)
.open(&path)?;
// A previous owner may be mid-teardown (e.g. an `AsyncHDF5Memory`
// dropped without `shutdown()`: its background task releases the
// store a moment later), so give the lock a short, bounded grace
// period before reporting a genuine second writer.
let mut attempts_left = LOCK_RETRIES;
loop {
match file.try_lock() {
Ok(()) => return Ok(Self { _file: file }),
Err(TryLockError::WouldBlock) if attempts_left > 0 => {
attempts_left -= 1;
std::thread::sleep(LOCK_RETRY_DELAY);
}
Err(TryLockError::WouldBlock) => {
return Err(MemoryError::Locked(format!(
"{} is already open in this or another process (lock file {})",
store.display(),
path.display()
)));
}
Err(TryLockError::Error(e)) => return Err(MemoryError::Io(e)),
}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn second_acquire_fails_until_first_is_dropped() {
let dir = tempfile::TempDir::new().unwrap();
let store = dir.path().join("s.h5");
let first = StoreLock::acquire(&store).unwrap();
assert!(matches!(
StoreLock::acquire(&store),
Err(MemoryError::Locked(_))
));
drop(first);
StoreLock::acquire(&store).unwrap();
}
}
crates/clawhdf5-agent/src/strategy.rs
+3 -39
View File
@@ -167,17 +167,10 @@ pub fn auto_select_strategy(num_vectors: usize, hw: &HardwareCapabilities) -> Se
/// This dispatches to the appropriate search implementation based on the
/// selected strategy. For IVF-PQ, an index must be provided externally
/// (this function uses brute-force fallback if no IVF-PQ index is available).
///
/// `vectors_flat` is `vectors` flattened into one contiguous `[N × dim]`
/// row-major buffer (e.g. `MemoryCache::embeddings_flat`, maintained
/// incrementally alongside `vectors`). It's only consulted by the
/// `Blas`/`Accelerate` strategies, which otherwise re-flatten the whole
/// corpus on every call — passing the already-flat buffer skips that copy.
#[allow(clippy::too_many_arguments)]
pub fn search_with_metrics(
query: &[f32],
vectors: &[Vec<f32>],
vectors_flat: &[f32],
norms: &[f32],
tombstones: &[u8],
k: usize,
@@ -185,10 +178,6 @@ pub fn search_with_metrics(
#[cfg(feature = "gpu")] gpu_backend: Option<&crate::gpu_search::GpuSearchBackend>,
#[cfg(not(feature = "gpu"))] _gpu_backend: Option<&()>,
) -> (Vec<(usize, f32)>, SearchMetrics) {
// Only read by the Blas/Accelerate arms below, which are themselves
// feature-gated — reference it unconditionally so a build with neither
// feature enabled doesn't warn about an unused parameter.
let _ = vectors_flat;
let start = Instant::now();
let active_count = tombstones.iter().filter(|&&t| t == 0).count();
@@ -208,14 +197,7 @@ pub fn search_with_metrics(
gpu_active = false;
#[cfg(feature = "fast-math")]
{
crate::blas_search::blas_cosine_batch_flat(
query,
vectors_flat,
norms,
tombstones,
query.len(),
k,
)
crate::blas_search::blas_cosine_batch(query, vectors, norms, tombstones, k)
}
#[cfg(not(feature = "fast-math"))]
{
@@ -229,13 +211,8 @@ pub fn search_with_metrics(
gpu_active = false;
#[cfg(any(feature = "accelerate", feature = "openblas"))]
{
crate::accelerate_search::accelerate_cosine_batch(
query,
vectors_flat,
norms,
tombstones,
query.len(),
k,
crate::accelerate_search::accelerate_cosine_batch_vecs(
query, vectors, norms, tombstones, k,
)
}
#[cfg(not(any(feature = "accelerate", feature = "openblas")))]
@@ -348,10 +325,6 @@ mod tests {
(0..n).map(|_| (0..dim).map(|_| next()).collect()).collect()
}
fn flatten(vectors: &[Vec<f32>]) -> Vec<f32> {
vectors.iter().flatten().copied().collect()
}
// --- auto_select_strategy tests ---
#[test]
@@ -517,7 +490,6 @@ mod tests {
let (results, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
5,
@@ -548,7 +520,6 @@ mod tests {
let (results, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
10,
@@ -574,7 +545,6 @@ mod tests {
let (_, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
10,
@@ -600,7 +570,6 @@ mod tests {
let (results, _) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
10,
@@ -634,7 +603,6 @@ mod tests {
let (results, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
100,
@@ -679,7 +647,6 @@ mod tests {
let (_, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
5,
@@ -751,7 +718,6 @@ mod tests {
let (results, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
10,
@@ -778,7 +744,6 @@ mod tests {
let (results, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
10,
@@ -857,7 +822,6 @@ mod tests {
let (results, metrics) = search_with_metrics(
&query,
&vectors,
&flatten(&vectors),
&norms,
&tombstones,
10,
crates/clawhdf5-agent/src/vector_search.rs
+30 -75
View File
@@ -4,44 +4,6 @@
//! `clawhdf5_accel`, with optional float16 support via the `half` crate.
//! Supports pre-computed norms for eliminating redundant norm computations.
/// A corpus of equal-length embeddings addressable by index.
///
/// Lets the batch kernels read either the cache's flat `[N x dim]` buffer or a
/// plain `Vec<Vec<f32>>` without either side owning a second copy.
pub trait VectorSet {
/// Number of embeddings.
fn count(&self) -> usize;
/// Embedding `i`; callers only index below [`VectorSet::count`].
fn row(&self, i: usize) -> &[f32];
}
impl VectorSet for [Vec<f32>] {
fn count(&self) -> usize {
self.len()
}
fn row(&self, i: usize) -> &[f32] {
&self[i]
}
}
impl VectorSet for Vec<Vec<f32>> {
fn count(&self) -> usize {
self.len()
}
fn row(&self, i: usize) -> &[f32] {
&self[i]
}
}
impl VectorSet for crate::cache::Embeddings {
fn count(&self) -> usize {
self.len()
}
fn row(&self, i: usize) -> &[f32] {
&self[i]
}
}
/// Compute cosine similarity between two f32 slices.
///
/// Returns 0.0 if either vector has zero magnitude.
@@ -60,7 +22,7 @@ pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
/// Returns `(index, score)` pairs sorted by score descending.
pub fn cosine_similarity_batch(
query: &[f32],
vectors: &(impl VectorSet + ?Sized),
vectors: &[Vec<f32>],
tombstones: &[u8],
) -> Vec<(usize, f32)> {
let query_norm = clawhdf5_accel::vector_norm(query);
@@ -68,7 +30,7 @@ pub fn cosine_similarity_batch(
return Vec::new();
}
let n = vectors.count();
let n = vectors.len();
let mut results: Vec<(usize, f32)> = Vec::with_capacity(n);
// Process 4 vectors at a time where possible
@@ -80,9 +42,8 @@ pub fn cosine_similarity_batch(
if i < tombstones.len() && tombstones[i] != 0 {
continue;
}
let vec_norm = clawhdf5_accel::vector_norm(vectors.row(i));
let score =
crate::cosine_similarity_prenorm(query, query_norm, vectors.row(i), vec_norm);
let vec_norm = clawhdf5_accel::vector_norm(&vectors[i]);
let score = crate::cosine_similarity_prenorm(query, query_norm, &vectors[i], vec_norm);
results.push((i, score));
}
}
@@ -92,8 +53,8 @@ pub fn cosine_similarity_batch(
if i < tombstones.len() && tombstones[i] != 0 {
continue;
}
let vec_norm = clawhdf5_accel::vector_norm(vectors.row(i));
let score = crate::cosine_similarity_prenorm(query, query_norm, vectors.row(i), vec_norm);
let vec_norm = clawhdf5_accel::vector_norm(&vectors[i]);
let score = crate::cosine_similarity_prenorm(query, query_norm, &vectors[i], vec_norm);
results.push((i, score));
}
@@ -107,7 +68,7 @@ pub fn cosine_similarity_batch(
/// collections. Uses `score = dot(query, vec) / (query_norm * stored_norm)`.
pub fn cosine_similarity_batch_prenorm(
query: &[f32],
vectors: &(impl VectorSet + ?Sized),
vectors: &[Vec<f32>],
norms: &[f32],
tombstones: &[u8],
) -> Vec<(usize, f32)> {
@@ -116,7 +77,7 @@ pub fn cosine_similarity_batch_prenorm(
return Vec::new();
}
let n = vectors.count();
let n = vectors.len();
let mut results: Vec<(usize, f32)> = Vec::with_capacity(n);
for i in 0..n {
@@ -124,7 +85,7 @@ pub fn cosine_similarity_batch_prenorm(
continue;
}
let vec_norm = norms[i];
let score = crate::cosine_similarity_prenorm(query, query_norm, vectors.row(i), vec_norm);
let score = crate::cosine_similarity_prenorm(query, query_norm, &vectors[i], vec_norm);
results.push((i, score));
}
@@ -201,7 +162,7 @@ pub fn cosine_similarity_f16(
#[cfg(feature = "parallel")]
pub fn parallel_cosine_batch(
query: &[f32],
vectors: &(impl VectorSet + Sync + ?Sized),
vectors: &[Vec<f32>],
tombstones: &[u8],
k: usize,
) -> Vec<(usize, f32)> {
@@ -213,27 +174,24 @@ pub fn parallel_cosine_batch(
}
let num_cores = rayon::current_num_threads().max(1);
let chunk_size = vectors.count().div_ceil(num_cores);
let chunk_size = vectors.len().div_ceil(num_cores);
if chunk_size == 0 {
return Vec::new();
}
// Chunk over index ranges: the corpus may be one flat buffer rather than
// a slice of rows, so there is nothing to `par_chunks` over.
let n = vectors.count();
let mut all_results: Vec<(usize, f32)> = (0..n.div_ceil(chunk_size))
.into_par_iter()
.flat_map(|chunk_idx| {
let mut all_results: Vec<(usize, f32)> = vectors
.par_chunks(chunk_size)
.enumerate()
.flat_map(|(chunk_idx, chunk)| {
let base = chunk_idx * chunk_size;
let end = (base + chunk_size).min(n);
let mut local: Vec<(usize, f32)> = Vec::with_capacity(end - base);
for i in base..end {
let mut local: Vec<(usize, f32)> = Vec::with_capacity(chunk.len());
for (j, vec) in chunk.iter().enumerate() {
let i = base + j;
if i < tombstones.len() && tombstones[i] != 0 {
continue;
}
let vec_norm = clawhdf5_accel::vector_norm(vectors.row(i));
let score =
crate::cosine_similarity_prenorm(query, query_norm, vectors.row(i), vec_norm);
let vec_norm = clawhdf5_accel::vector_norm(vec);
let score = crate::cosine_similarity_prenorm(query, query_norm, vec, vec_norm);
local.push((i, score));
}
local.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
@@ -251,7 +209,7 @@ pub fn parallel_cosine_batch(
#[cfg(feature = "parallel")]
pub fn parallel_cosine_batch_prenorm(
query: &[f32],
vectors: &(impl VectorSet + Sync + ?Sized),
vectors: &[Vec<f32>],
norms: &[f32],
tombstones: &[u8],
k: usize,
@@ -264,26 +222,23 @@ pub fn parallel_cosine_batch_prenorm(
}
let num_cores = rayon::current_num_threads().max(1);
let chunk_size = vectors.count().div_ceil(num_cores);
let chunk_size = vectors.len().div_ceil(num_cores);
if chunk_size == 0 {
return Vec::new();
}
// Chunk over index ranges: the corpus may be one flat buffer rather than
// a slice of rows, so there is nothing to `par_chunks` over.
let n = vectors.count();
let mut all_results: Vec<(usize, f32)> = (0..n.div_ceil(chunk_size))
.into_par_iter()
.flat_map(|chunk_idx| {
let mut all_results: Vec<(usize, f32)> = vectors
.par_chunks(chunk_size)
.enumerate()
.flat_map(|(chunk_idx, chunk)| {
let base = chunk_idx * chunk_size;
let end = (base + chunk_size).min(n);
let mut local: Vec<(usize, f32)> = Vec::with_capacity(end - base);
for i in base..end {
let mut local: Vec<(usize, f32)> = Vec::with_capacity(chunk.len());
for (j, vec) in chunk.iter().enumerate() {
let i = base + j;
if i < tombstones.len() && tombstones[i] != 0 {
continue;
}
let score =
crate::cosine_similarity_prenorm(query, query_norm, vectors.row(i), norms[i]);
let score = crate::cosine_similarity_prenorm(query, query_norm, vec, norms[i]);
local.push((i, score));
}
local.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
crates/clawhdf5-agent/src/wal.rs
+40 -704
View File
@@ -13,57 +13,16 @@ use crate::MemoryError;
const WAL_MAGIC: [u8; 4] = [0x45, 0x48, 0x57, 0x4C]; // "EHWL"
/// Bytes before the first entry: [`WAL_MAGIC`] (4) + version (1) + entry
/// count (4). Named so the offset arithmetic in `open()` — which decides
/// where an append lands, and therefore whether it is replayable — reads as
/// a header length rather than a bare 9.
const WAL_HEADER_LEN: u64 = WAL_MAGIC.len() as u64 + 1 + 4;
/// Current WAL format version: every entry ends with a 4-byte CRC32 trailer
/// (see [`TeeReader`]) so a bit-flip is detected and replay stops there
/// instead of silently accepting corrupted data.
const WAL_VERSION: u8 = 2;
/// Current WAL format version: every entry's CRC32 trailer is computed over
/// its own bytes *chained with the previous entry's stored CRC*
/// (`crc32(entry_bytes ++ prev_crc.to_le_bytes())`, seeded with 0 for the
/// first entry after a truncation). A per-entry CRC alone only detects a
/// bit-flip within that entry; chaining additionally detects entries being
/// reordered, duplicated, or spliced (e.g. a Tombstone moved before/after
/// its target Save) — the moved/inserted entry's stored CRC was computed
/// against a different predecessor than the one now in front of it on disk,
/// so the chain breaks at that point and replay stops there.
const WAL_VERSION: u8 = 4;
/// The chained-CRC format before [`WalEntryType::Update`] records existed.
/// Byte-for-byte the same framing as [`WAL_VERSION`], so it is read by the
/// same code, and `WalFile::open` upgrades it in place by rewriting the
/// header's version byte (the header is not covered by the CRC chain).
///
/// The bump exists for *older binaries*: they don't know record type 0x04,
/// would treat it as a torn tail, and would truncate it — and everything
/// after it — away. An unknown header version makes them refuse the file
/// with a clear error instead.
const WAL_VERSION_CHAINED_NO_UPDATE: u8 = 3;
/// The previous WAL format version: still a CRC32 per entry (so a bit-flip
/// within one entry is caught), but not chained to the previous entry's CRC
/// (so reordering/splicing whole entries is not detected). Written by
/// versions of this crate before the chaining hardening. Fully supported for
/// reading via [`WalFile::read_entries`] — not restricted like
/// [`WAL_VERSION_LEGACY_NO_CRC`], since it still verifies each entry
/// individually. `WalFile::open` migrates it to [`WAL_VERSION`] by
/// recreating the file fresh, the same as the legacy-no-CRC migration below.
const WAL_VERSION_CRC_UNCHAINED: u8 = 2;
/// The oldest WAL version this crate still knows how to *read*: no
/// per-entry CRC trailer at all, so a bit-flip anywhere is silently
/// accepted. Written by versions of this crate before the CRC32 hardening.
/// Because of that — unlike [`WAL_VERSION_CRC_UNCHAINED`] — this version is
/// deliberately *not* reachable through the public [`WalFile::read_entries`]
/// API; only [`WalFile::read_entries_for_migration`] (used exclusively by
/// `HDF5Memory::open`'s one-time migration path) will parse it. Flipping a
/// version byte from 2/3 down to 1 no longer silently downgrades a file to
/// the fully-unverified parser for an arbitrary caller.
///
/// `WalFile::open` migrates a legacy file to [`WAL_VERSION`] by recreating
/// it fresh — safe because every real call site reads existing entries via
/// [`WalFile::read_entries_for_migration`] before calling `open` (see
/// The only other WAL version this crate still knows how to *read*: no
/// per-entry CRC trailer. Written by versions of this crate before the CRC32
/// hardening. `WalFile::open` migrates a legacy file to [`WAL_VERSION`] by
/// recreating it fresh — safe because every real call site reads existing
/// entries via [`WalFile::read_entries`] before calling `open` (see
/// `HDF5Memory::open`), so no data is lost.
const WAL_VERSION_LEGACY_NO_CRC: u8 = 1;
@@ -78,10 +37,6 @@ pub enum WalEntryType {
Save = 0x01,
Tombstone = 0x02,
ActivationUpdate = 0x03,
/// Replace the record at `update_index` in place (`save_or_update` hit).
/// Logged as a plain `Save` before this existed, so replay appended a
/// duplicate instead of updating.
Update = 0x04,
}
impl WalEntryType {
@@ -90,7 +45,6 @@ impl WalEntryType {
0x01 => Some(Self::Save),
0x02 => Some(Self::Tombstone),
0x03 => Some(Self::ActivationUpdate),
0x04 => Some(Self::Update),
_ => None,
}
}
@@ -107,8 +61,6 @@ pub struct WalEntry {
pub tags: String,
/// For tombstone entries: the index of the entry to delete.
pub tombstone_index: Option<usize>,
/// For update entries: the index of the record to replace.
pub update_index: Option<usize>,
}
/// How many entries to accumulate before updating the header entry_count.
@@ -125,77 +77,15 @@ pub struct WalFile {
entry_count: u32,
/// Entries written since the last header count update.
pending_header_sync: u32,
/// CRC32 chain state: the previous entry's stored CRC (0 if this file
/// has no entries yet), folded into the next entry's CRC computation.
/// Reset to 0 by `truncate()`/`create_fresh_wal_file`, and re-derived by
/// scanning existing entries when `open()` attaches to a non-empty file.
running_crc: u32,
/// Bytes of verified entries after the header (the length of the chain
/// `running_crc` covers). Together they form the [`WalMark`].
chain_len: u64,
}
/// What a WAL file's 9-byte header looks like, without reading any entries.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum WalHeaderStatus {
/// A version this build can read (current or legacy).
Readable,
/// Shorter than a header — e.g. a crash while the file was being created.
/// It cannot contain entries.
Torn,
/// Not a WAL file at all.
BadMagic,
/// Well-formed header from a version this build doesn't know — most
/// likely written by a *newer* build. Never discard this: the entries are
/// probably fine, this binary just can't read them.
UnknownVersion(u8),
}
/// Classify the header of the WAL at `path`.
pub fn wal_header_status(path: &Path) -> std::io::Result<WalHeaderStatus> {
let mut header = [0u8; WAL_HEADER_LEN as usize];
let mut f = File::open(path)?;
let mut filled = 0;
while filled < header.len() {
match f.read(&mut header[filled..])? {
0 => return Ok(WalHeaderStatus::Torn),
n => filled += n,
}
}
if header[0..4] != WAL_MAGIC {
return Ok(WalHeaderStatus::BadMagic);
}
Ok(match header[4] {
WAL_VERSION
| WAL_VERSION_CHAINED_NO_UPDATE
| WAL_VERSION_CRC_UNCHAINED
| WAL_VERSION_LEGACY_NO_CRC => WalHeaderStatus::Readable,
v => WalHeaderStatus::UnknownVersion(v),
})
}
/// A position in a WAL's CRC chain: `len` bytes of entries after the header,
/// whose chained CRC is `crc`.
///
/// A checkpoint stores the mark of the WAL prefix it folded into the `.h5`
/// file. If the process dies after the new `.h5` is in place but before the
/// WAL is truncated, the next `open()` finds that exact prefix still in the
/// WAL and skips it instead of replaying it on top of data that already
/// contains it (which used to duplicate every pending entry).
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct WalMark {
pub len: u64,
pub crc: u32,
}
impl WalFile {
/// Open or create a WAL file. If it exists, read the header and entry count.
///
/// A pre-chaining WAL file ([`WAL_VERSION_CRC_UNCHAINED`] or
/// [`WAL_VERSION_LEGACY_NO_CRC`]) is migrated to the current format by
/// recreating it fresh. Callers that need an existing file's entries must
/// call [`WalFile::read_entries`] (or, for a legacy-no-CRC file,
/// [`WalFile::read_entries_for_migration`]) first, before calling `open`.
/// A legacy (pre-CRC) WAL file is migrated to the current format by
/// recreating it fresh — see [`WAL_VERSION_LEGACY_NO_CRC`]. Callers that
/// need the legacy file's entries must call [`WalFile::read_entries`]
/// first, before calling `open`.
pub fn open(path: &Path) -> Result<Self, MemoryError> {
if path.exists() {
// Read existing header
@@ -212,71 +102,20 @@ impl WalFile {
let mut ver = [0u8; 1];
f.read_exact(&mut ver)?;
match ver[0] {
WAL_VERSION | WAL_VERSION_CHAINED_NO_UPDATE => {
if ver[0] == WAL_VERSION_CHAINED_NO_UPDATE {
// Same framing; stamp the current version so an older
// binary refuses this file rather than truncating an
// Update record it can't parse. See the constant.
f.seek(SeekFrom::Start(4))?;
f.write_all(&[WAL_VERSION])?;
f.seek(SeekFrom::Start(5))?;
}
WAL_VERSION => {
let mut count_buf = [0u8; 4];
f.read_exact(&mut count_buf)?;
let header_count = u32::from_le_bytes(count_buf);
// Scan any existing entries to resume the CRC chain
// correctly for further appends (the header's count may
// be stale from deferred group-commit sync, same
// tolerance `read_entries` already has, so the scanned
// count is also the more accurate of the two).
let (entries, running_crc, verified_bytes) =
read_chained_entries(&mut f, 0, None);
let entry_count = if entries.is_empty() {
header_count
} else {
entries.len() as u32
};
// Position the append at the end of the VERIFIED prefix,
// and drop anything after it.
//
// This used to `seek(End(0))`, which appends PAST a torn
// tail — the ordinary outcome of a crash mid-append. The
// new entry is then chained to the last good entry, but
// sits on disk behind the garbage:
//
// [1..N verified][torn bytes][N+1 chained to N]
//
// Replay stops at the torn bytes, so N+1 is unreachable
// FOREVER even though its `append` returned Ok and synced.
// That is silent data loss in the one situation a WAL
// exists for. Truncating to the verified end is the
// standard recovery: the torn tail was never acknowledged
// to any caller, so discarding it loses nothing, and the
// chain then continues from a byte offset that matches
// `running_crc`.
let verified_end = WAL_HEADER_LEN + verified_bytes;
let file_len = f.metadata()?.len();
if file_len > verified_end {
eprintln!(
"clawhdf5-agent: WAL {} has {} unverifiable byte(s) after entry {}; \
discarding them so appends stay replayable",
path.display(),
file_len - verified_end,
entries.len()
);
f.set_len(verified_end)?;
}
f.seek(SeekFrom::Start(verified_end))?;
let entry_count = u32::from_le_bytes(count_buf);
// Seek to end for appending
f.seek(SeekFrom::End(0))?;
Ok(Self {
path: path.to_path_buf(),
file: Some(f),
entry_count,
pending_header_sync: 0,
running_crc,
chain_len: verified_bytes,
})
}
WAL_VERSION_CRC_UNCHAINED | WAL_VERSION_LEGACY_NO_CRC => {
WAL_VERSION_LEGACY_NO_CRC => {
drop(f);
let f = create_fresh_wal_file(path)?;
Ok(Self {
@@ -284,8 +123,6 @@ impl WalFile {
file: Some(f),
entry_count: 0,
pending_header_sync: 0,
running_crc: 0,
chain_len: 0,
})
}
v => Err(MemoryError::Schema(format!("unsupported WAL version {v}"))),
@@ -297,8 +134,6 @@ impl WalFile {
file: Some(f),
entry_count: 0,
pending_header_sync: 0,
running_crc: 0,
chain_len: 0,
})
}
}
@@ -322,20 +157,8 @@ impl WalFile {
4 + entry.session_id.len() +
4 + entry.tags.len(),
);
match entry.update_index {
Some(index) => {
let index = u32::try_from(index).map_err(|_| {
MemoryError::Schema(format!("WAL update index {index} exceeds u32"))
})?;
buf.push(WalEntryType::Update as u8);
buf.extend_from_slice(&entry.timestamp.to_le_bytes());
buf.extend_from_slice(&index.to_le_bytes());
}
None => {
buf.push(WalEntryType::Save as u8);
buf.extend_from_slice(&entry.timestamp.to_le_bytes());
}
}
serialize_str(&mut buf, &entry.chunk);
buf.extend_from_slice(&(emb_len as u32).to_le_bytes());
for &val in &entry.embedding {
@@ -345,10 +168,7 @@ impl WalFile {
serialize_str(&mut buf, &entry.session_id);
serialize_str(&mut buf, &entry.tags);
// Chain this entry's CRC to the previous one's so reordering/
// splicing entries (not just flipping a bit within one) is detected
// on replay — see WAL_VERSION's doc comment.
let crc = chained_crc(&buf, self.running_crc);
let crc = crc32(&buf);
buf.extend_from_slice(&crc.to_le_bytes());
let f = self
@@ -356,9 +176,7 @@ impl WalFile {
.as_mut()
.ok_or_else(|| MemoryError::Io(std::io::Error::other("WAL file not open")))?;
f.write_all(&buf)?;
self.chain_len += buf.len() as u64;
self.running_crc = crc;
self.entry_count += 1;
self.pending_header_sync += 1;
if self.pending_header_sync >= GROUP_COMMIT_SIZE {
@@ -373,7 +191,7 @@ impl WalFile {
buf[0] = WalEntryType::Tombstone as u8;
buf[1..9].copy_from_slice(&timestamp.to_le_bytes());
buf[9..13].copy_from_slice(&(index as u32).to_le_bytes());
let crc = chained_crc(&buf[..13], self.running_crc);
let crc = crc32(&buf[..13]);
buf[13..17].copy_from_slice(&crc.to_le_bytes());
let f = self
@@ -381,9 +199,7 @@ impl WalFile {
.as_mut()
.ok_or_else(|| MemoryError::Io(std::io::Error::other("WAL file not open")))?;
f.write_all(&buf)?;
self.chain_len += buf.len() as u64;
self.running_crc = crc;
self.entry_count += 1;
self.pending_header_sync += 1;
if self.pending_header_sync >= GROUP_COMMIT_SIZE {
@@ -398,46 +214,9 @@ impl WalFile {
/// (and may be stale if written with deferred group-commit updates). This
/// tolerates both truncated files (crash mid-write) and stale header counts
/// (crash before the next group-commit header sync). On a `WAL_VERSION`
/// file, a broken CRC chain (bit-flip, or an entry reordered/duplicated/
/// spliced in) is treated the same way — replay stops there rather than
/// accepting corrupted or tampered data. `WAL_VERSION_CRC_UNCHAINED`
/// files are read the same way minus the chain check (each entry's own
/// CRC is still verified).
///
/// Does **not** read [`WAL_VERSION_LEGACY_NO_CRC`] files — that format has
/// no integrity verification at all, so it's only reachable through
/// [`WalFile::read_entries_for_migration`], used exclusively by
/// `HDF5Memory::open`'s one-time migration path. Calling this on a
/// legacy-no-CRC file returns a typed error instead of silently
/// downgrading to the unverified parser.
/// file, a CRC32 mismatch on an entry is treated the same way — replay
/// stops there rather than accepting corrupted data.
pub fn read_entries(path: &Path) -> Result<Vec<WalEntry>, MemoryError> {
Self::read_entries_impl(path, false, None)
}
/// Like [`WalFile::read_entries`], but also accepts
/// [`WAL_VERSION_LEGACY_NO_CRC`] files (no per-entry integrity check at
/// all). Restricted to `pub(crate)` and named accordingly: the only
/// legitimate caller is `HDF5Memory::open`'s one-time migration of a
/// pre-CRC WAL file, which immediately recreates it in the current
/// format afterward. Do not use this for anything else.
///
/// `applied` is the checkpoint mark read from the `.h5` file, if any: if
/// the WAL's chain passes through it (same byte length, same chained
/// CRC), everything up to that point is already in the `.h5` and is
/// dropped. If it never does — the normal case, because the WAL was
/// truncated after the checkpoint — every entry is returned.
pub(crate) fn read_entries_for_migration(
path: &Path,
applied: Option<WalMark>,
) -> Result<Vec<WalEntry>, MemoryError> {
Self::read_entries_impl(path, true, applied)
}
fn read_entries_impl(
path: &Path,
allow_legacy_no_crc: bool,
applied: Option<WalMark>,
) -> Result<Vec<WalEntry>, MemoryError> {
if !path.exists() {
return Ok(Vec::new());
}
@@ -450,16 +229,10 @@ impl WalFile {
}
// entry_count is a pre-allocation hint only — we read until EOF.
let entry_count_hint = u32::from_le_bytes([header[5], header[6], header[7], header[8]]);
let mut entries = Vec::with_capacity(entry_count_hint as usize);
match header[4] {
WAL_VERSION | WAL_VERSION_CHAINED_NO_UPDATE => {
let (entries, _final_crc, _verified_bytes) =
read_chained_entries(&mut f, 0, applied);
Ok(entries)
}
WAL_VERSION_CRC_UNCHAINED => {
let mut entries = Vec::with_capacity(entry_count_hint as usize);
loop {
WAL_VERSION => loop {
let raw_and_result = {
let mut tee = TeeReader::new(&mut f);
let result = read_one_entry(&mut tee);
@@ -476,37 +249,27 @@ impl WalFile {
}
let stored_crc = u32::from_le_bytes(crc_buf);
if crc32(&raw) != stored_crc {
// Corruption detected — stop replay here, same as a
// clean truncation/EOF, rather than accepting the bad
// entry.
// Corruption detected — stop replay here, same as a clean
// truncation/EOF, rather than accepting the bad entry.
break;
}
if let Some(entry) = entry_opt {
entries.push(entry);
}
}
Ok(entries)
}
WAL_VERSION_LEGACY_NO_CRC if allow_legacy_no_crc => {
let mut entries = Vec::with_capacity(entry_count_hint as usize);
loop {
},
WAL_VERSION_LEGACY_NO_CRC => loop {
match read_one_entry(&mut f) {
Err(()) => break,
Ok(Some(entry)) => entries.push(entry),
Ok(None) => {}
}
},
v => {
return Err(MemoryError::Schema(format!("unsupported WAL version {v}")));
}
}
Ok(entries)
}
WAL_VERSION_LEGACY_NO_CRC => Err(MemoryError::Schema(
"WAL file is in the legacy no-CRC format (version 1), which read_entries() no \
longer accepts — it has no per-entry integrity verification. Only the one-time \
migration path (WalFile::open) can read and upgrade it."
.into(),
)),
v => Err(MemoryError::Schema(format!("unsupported WAL version {v}"))),
}
}
/// Truncate the WAL (after merge into .h5).
pub fn truncate(&mut self) -> Result<(), MemoryError> {
@@ -516,20 +279,9 @@ impl WalFile {
self.file = Some(f);
self.entry_count = 0;
self.pending_header_sync = 0;
self.running_crc = 0;
self.chain_len = 0;
Ok(())
}
/// The mark covering every entry currently in this WAL. Store it with a
/// checkpoint taken from the state those entries produced.
pub fn mark(&self) -> WalMark {
WalMark {
len: self.chain_len,
crc: self.running_crc,
}
}
/// Number of pending entries.
pub fn pending_count(&self) -> u32 {
self.entry_count
@@ -569,28 +321,6 @@ pub fn replay_into_cache(entries: &[WalEntry], cache: &mut crate::cache::MemoryC
entry.tags.clone(),
);
}
WalEntryType::Update => match entry.update_index {
// The index was valid when the record was written; if the
// store no longer has it, keep the data rather than drop it.
Some(idx) if idx < cache.len() => cache.update(
idx,
entry.chunk.clone(),
entry.embedding.clone(),
entry.source_channel.clone(),
entry.timestamp,
entry.session_id.clone(),
),
_ => {
cache.push(
entry.chunk.clone(),
entry.embedding.clone(),
entry.source_channel.clone(),
entry.timestamp,
entry.session_id.clone(),
entry.tags.clone(),
);
}
},
WalEntryType::Tombstone => {
if let Some(idx) = entry.tombstone_index {
cache.mark_deleted(idx);
@@ -643,81 +373,6 @@ fn read_embedding<R: Read>(f: &mut R) -> Result<Vec<f32>, MemoryError> {
Ok(vals)
}
/// Compute the CRC32 trailer for a `WAL_VERSION` entry, chaining in the
/// previous entry's stored CRC (0 for the first entry after a truncation).
fn chained_crc(entry_bytes: &[u8], prev_crc: u32) -> u32 {
let mut chained = Vec::with_capacity(entry_bytes.len() + 4);
chained.extend_from_slice(entry_bytes);
chained.extend_from_slice(&prev_crc.to_le_bytes());
crc32(&chained)
}
/// Read and verify all entries from a `WAL_VERSION` (chained-CRC) stream
/// starting at the reader's current position, given the chain state to
/// resume from (0 for a stream starting at the beginning of a fresh WAL).
///
/// Returns the parsed entries, the final running CRC — the chain state to
/// continue from for further appends — and the number of BYTES consumed by
/// those verified entries. Stops (without erroring) at the first entry that
/// fails to parse or whose stored CRC doesn't match the expected chain value
/// — a bit-flip, truncation/EOF, or an entry having been
/// reordered/duplicated/spliced all produce a chain mismatch at that point,
/// and are all handled the same way: replay stops there.
///
/// The byte count is what lets `open()` position an append at the end of the
/// VERIFIED prefix rather than at end-of-file. Appending past a torn tail
/// writes entries that replay can never reach — see `open`.
///
/// `applied`, when given, is a checkpoint mark: once the chain reaches exactly
/// that position, the entries collected so far are discarded (they are
/// already in the `.h5` file). A zero-length mark matches nothing.
fn read_chained_entries<R: Read>(
f: &mut R,
start_crc: u32,
applied: Option<WalMark>,
) -> (Vec<WalEntry>, u32, u64) {
let applied = applied.filter(|m| m.len > 0);
let mut entries = Vec::new();
let mut running_crc = start_crc;
let mut verified_bytes: u64 = 0;
loop {
let raw_and_result = {
let mut tee = TeeReader::new(f);
let result = read_one_entry(&mut tee);
(tee.into_buf(), result)
};
let (raw, result) = raw_and_result;
let entry_opt = match result {
Err(()) => break,
Ok(v) => v,
};
let mut crc_buf = [0u8; 4];
if f.read_exact(&mut crc_buf).is_err() {
break;
}
let stored_crc = u32::from_le_bytes(crc_buf);
if chained_crc(&raw, running_crc) != stored_crc {
break;
}
running_crc = stored_crc;
// Only counted once the entry AND its CRC trailer verified, so the
// offset always points just past a complete, checked entry.
verified_bytes += raw.len() as u64 + crc_buf.len() as u64;
if let Some(entry) = entry_opt {
entries.push(entry);
}
if applied
== Some(WalMark {
len: verified_bytes,
crc: running_crc,
})
{
entries.clear();
}
}
(entries, running_crc, verified_bytes)
}
/// Create a fresh WAL file at `path` with the current-version header,
/// truncating/overwriting anything already there.
fn create_fresh_wal_file(path: &Path) -> Result<File, MemoryError> {
@@ -775,14 +430,7 @@ fn read_one_entry<R: Read>(r: &mut R) -> Result<Option<WalEntry>, ()> {
let timestamp = f64::from_le_bytes(ts_buf);
match entry_type {
WalEntryType::Save | WalEntryType::Update => {
let update_index = if entry_type == WalEntryType::Update {
let mut idx_buf = [0u8; 4];
r.read_exact(&mut idx_buf).map_err(|_| ())?;
Some(u32::from_le_bytes(idx_buf) as usize)
} else {
None
};
WalEntryType::Save => {
let chunk = read_len_prefixed_str(r).map_err(|_| ())?;
let embedding = read_embedding(r).map_err(|_| ())?;
let source_channel = read_len_prefixed_str(r).map_err(|_| ())?;
@@ -797,7 +445,6 @@ fn read_one_entry<R: Read>(r: &mut R) -> Result<Option<WalEntry>, ()> {
session_id,
tags,
tombstone_index: None,
update_index,
}))
}
WalEntryType::Tombstone => {
@@ -813,7 +460,6 @@ fn read_one_entry<R: Read>(r: &mut R) -> Result<Option<WalEntry>, ()> {
session_id: String::new(),
tags: String::new(),
tombstone_index: Some(idx),
update_index: None,
}))
}
WalEntryType::ActivationUpdate => Ok(None),
@@ -837,7 +483,6 @@ mod tests {
session_id: "sess-001".to_string(),
tags: "tag1,tag2".to_string(),
tombstone_index: None,
update_index: None,
}
}
@@ -956,7 +601,7 @@ mod tests {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("test.h5.wal");
let unicode_chunk = "Hello 世界! 🌍 émojis & ünïcödé";
let embedding = vec![0.1, -0.2, 3.4567, f32::MAX, f32::MIN_POSITIVE];
let embedding = vec![0.1, -0.2, 3.14159, f32::MAX, f32::MIN_POSITIVE];
{
let mut wal = WalFile::open(&wal_path).unwrap();
let entry = WalEntry {
@@ -968,7 +613,6 @@ mod tests {
session_id: "sess-öö-123".to_string(),
tags: "α,β,γ".to_string(),
tombstone_index: None,
update_index: None,
};
wal.append_save(&entry).unwrap();
}
@@ -1103,148 +747,6 @@ mod tests {
assert!(entries.is_empty());
}
/// Reopen `path` and return the stored chunks in order.
fn reopen_chunks(path: &std::path::Path) -> Vec<String> {
let mem = HDF5Memory::open(path).unwrap();
mem.cache.chunks.clone()
}
#[test]
fn crash_between_checkpoint_and_wal_truncate_does_not_duplicate() {
// flush() writes the new .h5 and only then truncates the WAL. Dying in
// between leaves BOTH a .h5 that contains the pending entries and a
// WAL that still lists them; replaying blindly used to double them.
let dir = TempDir::new().unwrap();
let config = make_config(&dir);
let h5_path = config.path.clone();
let wal_path = h5_path.with_extension("h5.wal");
let stale_wal = dir.path().join("stale.wal");
{
let mut mem = HDF5Memory::create(config).unwrap();
for name in ["a", "b", "c"] {
mem.save(make_entry(name, &[1.0, 0.0, 0.0, 0.0])).unwrap();
}
assert_eq!(mem.wal_pending_count(), 3);
std::fs::copy(&wal_path, &stale_wal).unwrap();
mem.flush_wal().unwrap();
}
// Undo the truncate: this is the on-disk state right after the crash.
std::fs::copy(&stale_wal, &wal_path).unwrap();
assert_eq!(WalFile::read_entries(&wal_path).unwrap().len(), 3);
assert_eq!(reopen_chunks(&h5_path), ["a", "b", "c"]);
// Entries appended to that same WAL after recovery are still replayed.
{
let mut mem = HDF5Memory::open(&h5_path).unwrap();
mem.save(make_entry("d", &[0.0, 1.0, 0.0, 0.0])).unwrap();
}
assert_eq!(reopen_chunks(&h5_path), ["a", "b", "c", "d"]);
}
#[test]
fn entries_written_after_a_completed_checkpoint_are_all_replayed() {
// Normal case: the checkpoint's mark refers to a WAL that has since
// been truncated, so it must not suppress anything in the new one —
// including when the new WAL grows past the old mark's length.
let dir = TempDir::new().unwrap();
let config = make_config(&dir);
let h5_path = config.path.clone();
{
let mut mem = HDF5Memory::create(config).unwrap();
mem.save(make_entry("a", &[1.0, 0.0, 0.0, 0.0])).unwrap();
mem.flush_wal().unwrap();
for name in ["b", "c", "d"] {
mem.save(make_entry(name, &[1.0, 0.0, 0.0, 0.0])).unwrap();
}
}
assert_eq!(reopen_chunks(&h5_path), ["a", "b", "c", "d"]);
}
#[test]
fn save_or_update_replays_as_update_not_duplicate() {
let dir = TempDir::new().unwrap();
let config = make_config(&dir);
let h5_path = config.path.clone();
{
let mut mem = HDF5Memory::create(config).unwrap();
let mut first = make_entry("v1", &[1.0, 0.0, 0.0, 0.0]);
first.tags = "key".into();
let mut second = make_entry("v2", &[0.0, 1.0, 0.0, 0.0]);
second.tags = "key".into();
let a = mem.save_or_update(first).unwrap();
mem.save(make_entry("other", &[0.0, 0.0, 1.0, 0.0]))
.unwrap();
let b = mem.save_or_update(second).unwrap();
assert_eq!(a, b);
assert_eq!(mem.cache.chunks, ["v2", "other"]);
// Dropped without a checkpoint: all three records live in the WAL.
}
let mem = HDF5Memory::open(&h5_path).unwrap();
assert_eq!(mem.cache.chunks, ["v2", "other"]);
assert_eq!(mem.cache.embeddings[0], [0.0, 1.0, 0.0, 0.0]);
}
#[test]
fn v3_wal_is_read_and_upgraded_in_place() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("old.wal");
{
let mut wal = WalFile::open(&wal_path).unwrap();
wal.append_save(&make_wal_entry("kept", &[1.0])).unwrap();
}
// Rewrite the header as the pre-Update chained format.
let mut bytes = std::fs::read(&wal_path).unwrap();
bytes[4] = WAL_VERSION_CHAINED_NO_UPDATE;
std::fs::write(&wal_path, &bytes).unwrap();
assert_eq!(WalFile::read_entries(&wal_path).unwrap().len(), 1);
{
let mut wal = WalFile::open(&wal_path).unwrap();
assert_eq!(wal.pending_count(), 1);
wal.append_save(&make_wal_entry("new", &[2.0])).unwrap();
}
assert_eq!(std::fs::read(&wal_path).unwrap()[4], WAL_VERSION);
let chunks: Vec<_> = WalFile::read_entries(&wal_path)
.unwrap()
.into_iter()
.map(|e| e.chunk)
.collect();
assert_eq!(chunks, ["kept", "new"]);
}
#[test]
fn mark_matching_is_exact() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("m.wal");
let mut wal = WalFile::open(&wal_path).unwrap();
wal.append_save(&make_wal_entry("one", &[1.0])).unwrap();
let after_one = wal.mark();
wal.append_save(&make_wal_entry("two", &[2.0])).unwrap();
let after_two = wal.mark();
drop(wal);
let read = |m| {
WalFile::read_entries_for_migration(&wal_path, m)
.unwrap()
.into_iter()
.map(|e| e.chunk)
.collect::<Vec<_>>()
};
assert_eq!(read(None), ["one", "two"]);
assert_eq!(read(Some(after_one)), ["two"]);
assert!(read(Some(after_two)).is_empty());
// Right length, wrong CRC (a different WAL generation): skip nothing.
let foreign = WalMark {
crc: after_one.crc ^ 1,
..after_one
};
assert_eq!(read(Some(foreign)), ["one", "two"]);
// Reopening resumes the same mark.
assert_eq!(WalFile::open(&wal_path).unwrap().mark(), after_two);
}
#[test]
fn test_wal_replay_on_open() {
// Test WAL replay using read_entries + replay_into_cache directly,
@@ -1410,157 +912,16 @@ mod tests {
assert_eq!(entries[0].chunk, "first");
}
/// A crash mid-append leaves a torn final entry. Reopening the WAL must
/// place the next append at the end of the VERIFIED prefix, not at
/// end-of-file, or that append is written behind garbage the replay
/// scanner stops at — unreachable forever despite having returned Ok.
///
/// This is the ordinary crash case, so getting it wrong loses
/// acknowledged writes in exactly the situation a WAL exists for.
#[test]
fn test_wal_append_after_torn_tail_stays_replayable() {
fn test_wal_reads_legacy_v1_format_without_crc() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("test.h5.wal");
let mut wal = WalFile::open(&wal_path).unwrap();
wal.append_save(&make_wal_entry("first", &[1.0, 2.0]))
.unwrap();
drop(wal);
// Simulate the crash: a partial entry appended after the good one.
{
use std::io::Write;
let mut f = std::fs::OpenOptions::new()
.append(true)
.open(&wal_path)
.unwrap();
f.write_all(&[0xAB, 0xCD, 0xEF, 0x01, 0x02]).unwrap();
f.flush().unwrap();
}
// Reopen and append. The torn bytes must not survive between the
// verified prefix and the new entry.
let mut wal = WalFile::open(&wal_path).unwrap();
wal.append_save(&make_wal_entry("second", &[3.0, 4.0]))
.unwrap();
drop(wal);
let entries = WalFile::read_entries(&wal_path).unwrap();
assert_eq!(
entries.len(),
2,
"the append after a torn tail must be replayable; got {} entr(y/ies) — \
the post-crash write was silently lost",
entries.len()
);
}
/// Reordering two entries on disk must break the CRC chain — the
/// second entry's stored CRC was computed against the first entry's
/// real CRC, not against the chain state a reader sees after swapping
/// them, so replay stops immediately instead of accepting the tampered
/// order (INT-09).
#[test]
fn test_wal_detects_reordered_entries() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("test.h5.wal");
let mut wal = WalFile::open(&wal_path).unwrap();
wal.append_save(&make_wal_entry("first", &[1.0, 2.0]))
.unwrap();
let len_after_first = std::fs::metadata(&wal_path).unwrap().len() as usize;
wal.append_save(&make_wal_entry("second", &[3.0, 4.0]))
.unwrap();
let len_after_second = std::fs::metadata(&wal_path).unwrap().len() as usize;
drop(wal);
let bytes = std::fs::read(&wal_path).unwrap();
let header_len = 9usize;
let entry1_bytes = bytes[header_len..len_after_first].to_vec();
let entry2_bytes = bytes[len_after_first..len_after_second].to_vec();
let mut spliced = bytes[..header_len].to_vec();
spliced.extend_from_slice(&entry2_bytes);
spliced.extend_from_slice(&entry1_bytes);
std::fs::write(&wal_path, &spliced).unwrap();
let entries = WalFile::read_entries(&wal_path).unwrap();
assert!(
entries.is_empty(),
"reordered entries must break the CRC chain and stop replay, got {} entries",
entries.len()
);
}
/// Splicing a third-party entry in between two legitimate entries (e.g.
/// moving a Tombstone in front of the Save it's meant to follow) must
/// also break the chain for everything after the splice point.
#[test]
fn test_wal_detects_spliced_entry() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("test.h5.wal");
let mut wal = WalFile::open(&wal_path).unwrap();
wal.append_save(&make_wal_entry("first", &[1.0])).unwrap();
let len_after_first = std::fs::metadata(&wal_path).unwrap().len() as usize;
wal.append_save(&make_wal_entry("second", &[2.0])).unwrap();
let len_after_second = std::fs::metadata(&wal_path).unwrap().len() as usize;
wal.append_save(&make_wal_entry("third", &[3.0])).unwrap();
drop(wal);
let bytes = std::fs::read(&wal_path).unwrap();
let entry2_bytes = bytes[len_after_first..len_after_second].to_vec();
// Duplicate "second" right after itself: [first][second][second][third]
let mut spliced = bytes[..len_after_second].to_vec();
spliced.extend_from_slice(&entry2_bytes);
spliced.extend_from_slice(&bytes[len_after_second..]);
std::fs::write(&wal_path, &spliced).unwrap();
let entries = WalFile::read_entries(&wal_path).unwrap();
assert_eq!(
entries.len(),
2,
"replay must stop at the spliced duplicate, keeping only the entries before it"
);
assert_eq!(entries[0].chunk, "first");
assert_eq!(entries[1].chunk, "second");
}
/// A WAL closed (without truncating) and reopened must continue the CRC
/// chain correctly for newly appended entries — this is the normal
/// crash-restart-without-flush scenario (`HDF5Memory::open` replays
/// existing entries, then reopens the same file for further appends
/// without clearing it), and must not produce a false "reordering"
/// detection for its own legitimately-appended entries.
#[test]
fn test_wal_chain_continues_across_reopen() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("test.h5.wal");
let mut wal = WalFile::open(&wal_path).unwrap();
wal.append_save(&make_wal_entry("first", &[1.0])).unwrap();
drop(wal); // simulate a restart without ever truncating the WAL
let mut wal2 = WalFile::open(&wal_path).unwrap();
wal2.append_save(&make_wal_entry("second", &[2.0])).unwrap();
drop(wal2);
let entries = WalFile::read_entries(&wal_path).unwrap();
assert_eq!(
entries.len(),
2,
"both pre- and post-reopen entries must replay cleanly"
);
assert_eq!(entries[0].chunk, "first");
assert_eq!(entries[1].chunk, "second");
}
/// Build a legacy (WAL_VERSION_LEGACY_NO_CRC) WAL file containing one
/// Save entry, with no trailing CRC32.
fn build_legacy_v1_wal_bytes() -> Vec<u8> {
let wal_path = dir.path().join("legacy.h5.wal");
let mut buf = Vec::new();
buf.extend_from_slice(&WAL_MAGIC);
buf.push(WAL_VERSION_LEGACY_NO_CRC);
buf.extend_from_slice(&1u32.to_le_bytes());
// One Save entry in the old format: type + timestamp + fields, with
// no trailing CRC32.
buf.push(WalEntryType::Save as u8);
buf.extend_from_slice(&42.0f64.to_le_bytes());
serialize_str(&mut buf, "legacy-chunk");
@@ -1572,39 +933,14 @@ mod tests {
serialize_str(&mut buf, "chan");
serialize_str(&mut buf, "sess");
serialize_str(&mut buf, "tags");
buf
}
std::fs::write(&wal_path, &buf).unwrap();
#[test]
fn test_wal_reads_legacy_v1_format_without_crc() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("legacy.h5.wal");
std::fs::write(&wal_path, build_legacy_v1_wal_bytes()).unwrap();
// Only the migration-only reader may read a legacy no-CRC file.
let entries = WalFile::read_entries_for_migration(&wal_path, None).unwrap();
let entries = WalFile::read_entries(&wal_path).unwrap();
assert_eq!(entries.len(), 1);
assert_eq!(entries[0].chunk, "legacy-chunk");
assert_eq!(entries[0].embedding, vec![1.0, 2.0]);
}
/// The public `read_entries` must reject a legacy no-CRC file instead of
/// silently downgrading to the fully-unverified parser (INT-09) — flipping
/// a version byte from 2/3 down to 1 must not be a way to bypass every
/// integrity check for an arbitrary caller of the public API.
#[test]
fn test_wal_read_entries_rejects_legacy_v1_format() {
let dir = TempDir::new().unwrap();
let wal_path = dir.path().join("legacy.h5.wal");
std::fs::write(&wal_path, build_legacy_v1_wal_bytes()).unwrap();
let result = WalFile::read_entries(&wal_path);
assert!(
result.is_err(),
"read_entries() must reject a legacy no-CRC WAL file, not silently parse it"
);
}
#[test]
fn test_wal_open_migrates_legacy_v1_to_current_version() {
let dir = TempDir::new().unwrap();
crates/clawhdf5-agent/tests/crash_recovery.rs
-187
View File
@@ -1,187 +0,0 @@
//! Crash-recovery matrix for `HDF5Memory`.
//!
//! A process crash leaves whatever reached the OS on disk. These tests build
//! the on-disk images such a crash can leave behind — after every operation,
//! inside the checkpoint window (new `.h5` in place, WAL not yet truncated),
//! and with the WAL torn at every possible length — then reopen each image
//! and check the recovered store against a model of what was acknowledged.
//!
//! Invariants:
//! * never a duplicated or invented record;
//! * an image taken between operations recovers *exactly* the acknowledged
//! state;
//! * a torn WAL recovers the last checkpoint plus a prefix of the operations
//! logged since.
use std::path::{Path, PathBuf};
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry};
use tempfile::TempDir;
struct Rng(u64);
impl Rng {
fn next(&mut self) -> u64 {
self.0 = self.0.wrapping_add(0x9E37_79B9_7F4A_7C15);
let mut z = self.0;
z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
z ^ (z >> 31)
}
fn below(&mut self, n: usize) -> usize {
(self.next() % n.max(1) as u64) as usize
}
}
fn entry(chunk: &str, tags: &str) -> MemoryEntry {
MemoryEntry {
chunk: chunk.to_string(),
embedding: vec![1.0, 0.0, 0.0, 0.0],
source_channel: "test".into(),
timestamp: 1.0,
session_id: "s".into(),
tags: tags.to_string(),
}
}
fn wal_path(h5: &Path) -> PathBuf {
h5.with_extension("h5.wal")
}
/// Copy the store (`.h5` + WAL) into a fresh directory, as a crash image.
fn image(h5: &Path, into: &TempDir, name: &str) -> PathBuf {
let dest = into.path().join(format!("{name}.h5"));
std::fs::copy(h5, &dest).unwrap();
if wal_path(h5).exists() {
std::fs::copy(wal_path(h5), wal_path(&dest)).unwrap();
}
dest
}
fn recovered(h5: &Path) -> Vec<String> {
// Read-only: the image must not be modified, and no lock is needed.
HDF5Memory::open_read_only(h5).unwrap().cache.chunks.clone()
}
/// Apply one random operation to the store and to the model.
fn step(mem: &mut HDF5Memory, model: &mut Vec<String>, rng: &mut Rng, n: usize) {
match rng.below(6) {
0 => mem.flush_wal().unwrap(),
1 if !model.is_empty() => {
// Update an existing record in place, addressed by its tag.
let idx = rng.below(model.len());
let chunk = format!("u{n}");
assert_eq!(
mem.save_or_update(entry(&chunk, &format!("tag{idx}")))
.unwrap(),
idx
);
model[idx] = chunk;
}
_ => {
let chunk = format!("c{n}");
mem.save(entry(&chunk, &format!("tag{}", model.len())))
.unwrap();
model.push(chunk);
}
}
}
#[test]
fn image_after_every_operation_recovers_the_acknowledged_state() {
for seed in 0..40u64 {
let mut rng = Rng(seed);
let dir = TempDir::new().unwrap();
let images = TempDir::new().unwrap();
let mut config = MemoryConfig::new(dir.path().join("store.h5"), "agent", 4);
config.wal_enabled = true;
config.wal_max_entries = 1 + rng.below(6); // force frequent checkpoints
let h5 = config.path.clone();
let mut mem = HDF5Memory::create(config).unwrap();
let mut model = Vec::new();
for n in 0..30 {
step(&mut mem, &mut model, &mut rng, n);
let img = image(&h5, &images, &format!("s{seed}-{n}"));
assert_eq!(recovered(&img), model, "seed {seed}, after op {n}");
}
}
}
#[test]
fn crash_inside_the_checkpoint_window_never_duplicates() {
for seed in 0..40u64 {
let mut rng = Rng(seed ^ 0xABCD);
let dir = TempDir::new().unwrap();
let images = TempDir::new().unwrap();
let mut config = MemoryConfig::new(dir.path().join("store.h5"), "agent", 4);
config.wal_enabled = true;
config.wal_max_entries = 1000; // checkpoints only when we ask
let h5 = config.path.clone();
let mut mem = HDF5Memory::create(config).unwrap();
let mut model = Vec::new();
for round in 0..4 {
for n in 0..(1 + rng.below(6)) {
step(&mut mem, &mut model, &mut rng, round * 100 + n);
}
// The WAL as it is just before the checkpoint...
let stale_wal = images.path().join(format!("stale-{seed}-{round}.wal"));
if wal_path(&h5).exists() {
std::fs::copy(wal_path(&h5), &stale_wal).unwrap();
}
mem.flush_wal().unwrap();
// ...put back next to the NEW .h5: the crash-in-the-window image.
let img = image(&h5, &images, &format!("w{seed}-{round}"));
if stale_wal.exists() {
std::fs::copy(&stale_wal, wal_path(&img)).unwrap();
}
assert_eq!(recovered(&img), model, "seed {seed}, round {round}");
}
}
}
#[test]
fn torn_wal_recovers_checkpoint_plus_a_prefix() {
let dir = TempDir::new().unwrap();
let images = TempDir::new().unwrap();
let mut config = MemoryConfig::new(dir.path().join("store.h5"), "agent", 4);
config.wal_enabled = true;
config.wal_max_entries = 1000;
let h5 = config.path.clone();
let mut mem = HDF5Memory::create(config).unwrap();
for name in ["a", "b"] {
mem.save(entry(name, name)).unwrap();
}
mem.flush_wal().unwrap();
let checkpointed = vec!["a".to_string(), "b".to_string()];
// States the store passes through as each later op is logged.
let mut states = vec![checkpointed.clone()];
let mut model = checkpointed.clone();
mem.save(entry("c", "c")).unwrap();
model.push("c".into());
states.push(model.clone());
mem.save_or_update(entry("a2", "a")).unwrap();
model[0] = "a2".into();
states.push(model.clone());
mem.save(entry("d", "d")).unwrap();
model.push("d".into());
states.push(model.clone());
let full_wal = std::fs::read(wal_path(&h5)).unwrap();
let mut seen = std::collections::BTreeSet::new();
for len in 0..=full_wal.len() {
let img = image(&h5, &images, &format!("t{len}"));
std::fs::write(wal_path(&img), &full_wal[..len]).unwrap();
let got = recovered(&img);
let which = states
.iter()
.position(|s| *s == got)
.unwrap_or_else(|| panic!("WAL torn at {len} bytes recovered {got:?}"));
seen.insert(which);
}
// Every intermediate state is reachable, and the full WAL gives the last.
assert_eq!(seen.into_iter().collect::<Vec<_>>(), [0, 1, 2, 3]);
}
crates/clawhdf5-agent/tests/e2e_tests.rs
+10 -12
View File
@@ -196,7 +196,7 @@ fn test_migration_round_trip() {
mem.add_relation(e1, e2, "discusses", 0.8).unwrap();
// Verify all data transferred by reopening
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 500);
// Verify sessions
@@ -266,7 +266,7 @@ fn test_knowledge_graph_workflow() {
assert_eq!(entity.entity_type, "library");
// Persistence
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.knowledge().entities.len(), 4);
assert_eq!(reopened.knowledge().relations.len(), 4);
@@ -316,7 +316,7 @@ fn test_multi_session_workflow() {
assert_eq!(mem.count(), 100); // 5 sessions * 20 entries
// Reopen and verify sessions
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
for sess in 0..5 {
let summary = reopened
.get_session_summary(&format!("sess_{sess}"))
@@ -460,7 +460,7 @@ fn test_snapshot_and_continue() {
assert_eq!(snap_mem.count(), 50);
// Original should have 100
let orig_mem = HDF5Memory::open_read_only(&path).unwrap();
let orig_mem = HDF5Memory::open(&path).unwrap();
assert_eq!(orig_mem.count(), 100);
}
@@ -483,7 +483,7 @@ fn test_config_persistence_across_ops() {
mem.add_session("s1", 0, 0, "ch", "summary").unwrap();
mem.add_entity("Entity", "type", -1).unwrap();
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.config().embedding_dim, 128);
assert_eq!(reopened.config().embedder, "custom:my-embedder-v2");
assert_eq!(reopened.config().chunk_size, 2048);
@@ -695,7 +695,7 @@ fn test_large_text_chunks() {
mem.save_batch(entries).unwrap();
// Reopen and verify
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 10);
let (_, cache, _, _) = read_cache(&path);
@@ -752,7 +752,7 @@ fn test_interleaved_sessions_entries() {
mem.flush_wal().unwrap();
// Verify
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 6);
assert_eq!(
reopened.get_session_summary("s1").unwrap().as_deref(),
@@ -806,7 +806,7 @@ fn test_knowledge_graph_with_embeddings() {
mem.add_relation(e_python, e_hdf5, "reads", 0.9).unwrap();
// Verify entity-embedding linkage persists
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
let rust_entity = reopened.knowledge().get_entity(e_rust).unwrap();
assert_eq!(rust_entity.embedding_idx, idx0 as i64);
@@ -1048,7 +1048,7 @@ fn test_gpu_l2_fallback_works() {
let tombstones = vec![0u8; 3];
let gpu = clawhdf5_agent::gpu_search::GpuSearchBackend::try_init(&vectors, &norms, 2, 1);
let results = gpu.search_l2(&[0.0, 0.0], &vectors, &tombstones, 3);
let results = gpu.search_l2(&vec![0.0, 0.0], &vectors, &tombstones, 3);
assert_eq!(results.len(), 3);
assert_eq!(results[0].0, 0);
@@ -1099,7 +1099,7 @@ fn test_mmap_reader_direct_access() {
// Open via MmapReader directly
let mmap = clawhdf5_io::MmapReader::open(&path).unwrap();
assert!(!mmap.is_empty());
assert!(mmap.len() > 0);
// Verify we can read bytes at specific offsets
let bytes = mmap.read_at(0, 8);
assert!(bytes.is_some());
@@ -1144,11 +1144,9 @@ fn test_strategy_reports_backend() {
let tombstones = vec![0u8; n];
let query = vectors[0].clone();
let flat: Vec<f32> = vectors.iter().flatten().copied().collect();
let (_, metrics) = strategy::search_with_metrics(
&query,
&vectors,
&flat,
&norms,
&tombstones,
5,
crates/clawhdf5-agent/tests/fixtures/store_v2_5_0.h5
Vendored
BIN
View File
Binary file not shown.
crates/clawhdf5-agent/tests/float16_store.rs
-300
View File
@@ -1,300 +0,0 @@
//! `MemoryConfig::float16`: embeddings stored as IEEE half precision.
//!
//! The setting used to be recorded in `/meta` and otherwise ignored — the
//! embeddings dataset was always `f32`. These tests pin what it now does: the
//! dataset is `float16`, the in-memory cache holds exactly the values the file
//! holds (so search results survive a reopen bit for bit), and a value half
//! precision cannot represent is refused rather than stored as infinity.
use std::path::{Path, PathBuf};
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, MemoryError};
use clawhdf5_format::float16::round_to_f16;
use tempfile::TempDir;
const DIM: usize = 64;
/// Deterministic, embedding-like unit vectors.
fn embedding(seed: u64) -> Vec<f32> {
let mut x = seed.wrapping_mul(0x9E37_79B9_7F4A_7C15) | 1;
let v: Vec<f32> = (0..DIM)
.map(|_| {
x ^= x << 13;
x ^= x >> 7;
x ^= x << 17;
(x >> 40) as f32 / (1u64 << 24) as f32 - 0.5
})
.collect();
let norm = v.iter().map(|a| a * a).sum::<f32>().sqrt();
v.iter().map(|a| a / norm).collect()
}
fn entry(i: u64) -> MemoryEntry {
MemoryEntry {
chunk: format!("memory number {i} about topic {}", i % 7),
embedding: embedding(i),
source_channel: "test".into(),
timestamp: i as f64,
session_id: "s".into(),
tags: format!("t{i}"),
}
}
fn config(dir: &TempDir, name: &str, float16: bool) -> MemoryConfig {
let mut c = MemoryConfig::new(dir.path().join(name), "agent", DIM);
c.float16 = float16;
c
}
fn embeddings_dtype_and_values(path: &Path) -> (String, Vec<f32>) {
let file = clawhdf5::File::open(path).unwrap();
let ds = file.dataset("memory/embeddings").unwrap();
(format!("{:?}", ds.dtype().unwrap()), ds.read_f32().unwrap())
}
fn search_bits(m: &mut HDF5Memory, q: u64) -> Vec<(usize, u32)> {
m.hybrid_search(&embedding(q), "memory topic 3", 0.4, 0.6, 10)
.iter()
.map(|r| (r.index, r.score.to_bits()))
.collect()
}
#[test]
fn float16_store_writes_half_precision_and_reopens_identically() {
let dir = TempDir::new().unwrap();
// Two identical stores. Search is not read-only (it boosts the Hebbian
// activation of what it returns, and checkpoints persist that), so each
// is queried exactly once: one live, one after a checkpoint and reopen.
let live_cfg = config(&dir, "live.h5", true);
let cfg = config(&dir, "f16.h5", true);
let path: PathBuf = cfg.path.clone();
let mut live = HDF5Memory::create(live_cfg).unwrap();
live.save_batch((0..200).map(entry).collect()).unwrap();
let mut m = HDF5Memory::create(cfg).unwrap();
m.save_batch((0..200).map(entry).collect()).unwrap();
drop(m);
// On disk: a genuine float16 dataset holding the rounded inputs.
let (dtype, values) = embeddings_dtype_and_values(&path);
assert_eq!(dtype, "Other(\"float16\")");
let expected: Vec<u32> = (0..200)
.flat_map(|i| embedding(i).into_iter().map(|v| round_to_f16(v).to_bits()))
.collect();
let got: Vec<u32> = values.iter().map(|v| v.to_bits()).collect();
assert_eq!(got, expected);
// Reopened, the store answers exactly as the live one does: the cache
// held the half-rounded values before the checkpoint.
let mut reopened = HDF5Memory::open(&path).unwrap();
for q in 0..5 {
assert_eq!(
search_bits(&mut live, 1000 + q),
search_bits(&mut reopened, 1000 + q),
"query {q}"
);
}
}
#[test]
fn float16_halves_the_embeddings_on_disk() {
let dir = TempDir::new().unwrap();
let mut sizes = Vec::new();
for float16 in [false, true] {
let cfg = config(&dir, &format!("s{float16}.h5"), float16);
let path = cfg.path.clone();
let mut m = HDF5Memory::create(cfg).unwrap();
m.save_batch((0..2000).map(entry).collect()).unwrap();
drop(m);
sizes.push(std::fs::metadata(&path).unwrap().len());
}
let embedding_bytes_f32 = (2000 * DIM * 4) as u64;
let saved = sizes[0] - sizes[1];
// Half of the f32 embeddings, give or take metadata and alignment.
assert!(
saved.abs_diff(embedding_bytes_f32 / 2) < 16 * 1024,
"f32 {} B, f16 {} B, saved {saved} B, expected ~{} B",
sizes[0],
sizes[1],
embedding_bytes_f32 / 2
);
}
#[test]
fn f32_store_is_unchanged() {
let dir = TempDir::new().unwrap();
let cfg = config(&dir, "f32.h5", false);
let path = cfg.path.clone();
let mut m = HDF5Memory::create(cfg).unwrap();
m.save_batch((0..50).map(entry).collect()).unwrap();
drop(m);
let (dtype, values) = embeddings_dtype_and_values(&path);
assert_eq!(dtype, "F32");
let expected: Vec<f32> = (0..50).flat_map(embedding).collect();
assert_eq!(values, expected);
}
#[test]
fn out_of_range_values_are_refused_not_stored_as_infinity() {
let dir = TempDir::new().unwrap();
let mut cfg = config(&dir, "range.h5", true);
cfg.wal_enabled = true;
let path = cfg.path.clone();
let mut m = HDF5Memory::create(cfg).unwrap();
m.save(entry(1)).unwrap();
let mut bad = entry(2);
bad.embedding[5] = 70_000.0;
match m.save(bad.clone()) {
Err(MemoryError::InvalidEntry(msg)) => assert!(msg.contains("embedding[5]"), "{msg}"),
other => panic!("expected InvalidEntry, got {other:?}"),
}
assert!(matches!(
m.save_or_update(bad.clone()),
Err(MemoryError::InvalidEntry(_))
));
// A batch is all or nothing.
assert!(matches!(
m.save_batch(vec![entry(3), bad.clone(), entry(4)]),
Err(MemoryError::InvalidEntry(_))
));
assert_eq!(m.count(), 1);
// The largest finite half, and values that round down to it, are fine.
let mut edge = entry(5);
edge.embedding[0] = 65504.0;
edge.embedding[1] = -65519.0;
m.save(edge).unwrap();
assert_eq!(m.count(), 2);
drop(m);
// Nothing rejected reached the WAL or the file.
let m = HDF5Memory::open(&path).unwrap();
assert_eq!(m.count(), 2);
// An f32 store takes the same value as it always did.
let mut m32 = HDF5Memory::create(config(&dir, "range32.h5", false)).unwrap();
m32.save(bad).unwrap();
}
#[test]
fn wal_replay_rounds_like_a_live_save() {
let dir = TempDir::new().unwrap();
let mut cfg = config(&dir, "wal.h5", true);
cfg.wal_enabled = true;
cfg.wal_max_entries = 10_000; // keep everything in the WAL
let path = cfg.path.clone();
let mut m = HDF5Memory::create(cfg).unwrap();
for i in 0..30 {
m.save(entry(i)).unwrap();
}
let live = search_bits(&mut m, 77);
// Crash image: the .h5 is still the empty checkpoint; everything is in
// the WAL, which holds the caller's f32 values.
let crash = TempDir::new().unwrap();
let image = crash.path().join("image.h5");
std::fs::copy(&path, &image).unwrap();
std::fs::copy(
path.with_extension("h5.wal"),
image.with_extension("h5.wal"),
)
.unwrap();
drop(m);
let mut recovered = HDF5Memory::open(&image).unwrap();
assert_eq!(recovered.count(), 30);
assert_eq!(search_bits(&mut recovered, 77), live);
}
#[test]
fn new_stores_default_to_float16() {
let dir = TempDir::new().unwrap();
let path = dir.path().join("default.h5");
let mut m = HDF5Memory::create(MemoryConfig::new(path.clone(), "agent", DIM)).unwrap();
assert!(m.config().float16);
m.save_batch((0..10).map(entry).collect()).unwrap();
drop(m);
assert_eq!(embeddings_dtype_and_values(&path).0, "Other(\"float16\")");
assert!(HDF5Memory::open(&path).unwrap().config().float16);
}
#[test]
fn an_existing_f32_store_stays_f32() {
// Written by the v2.5.0 CLI, with `float16 = 0` in /meta (every agent
// store has recorded it). Flipping the default for new stores must not
// reach back and round an existing store's embeddings.
let dir = TempDir::new().unwrap();
let path = dir.path().join("legacy.h5");
std::fs::copy(
concat!(
env!("CARGO_MANIFEST_DIR"),
"/tests/fixtures/store_v2_5_0.h5"
),
&path,
)
.unwrap();
let before = embeddings_dtype_and_values(&path);
assert_eq!(before.0, "F32");
let mut m = HDF5Memory::open(&path).unwrap();
assert!(!m.config().float16, "an old store must reopen as f32");
let dim = m.config().embedding_dim;
let odd: Vec<f32> = (0..dim).map(|i| 0.1 + i as f32 * 1e-4).collect();
m.save_batch(vec![MemoryEntry {
chunk: "added after the upgrade".into(),
embedding: odd.clone(),
source_channel: "test".into(),
timestamp: 1.0,
session_id: "s".into(),
tags: String::new(),
}])
.unwrap();
drop(m);
// Checkpointed: still f32, the old rows untouched and the new one exact.
let (dtype, values) = embeddings_dtype_and_values(&path);
assert_eq!(dtype, "F32");
assert_eq!(&values[..before.1.len()], before.1.as_slice());
assert_eq!(&values[before.1.len()..], odd.as_slice());
}
/// `Group::attrs` leaves out an attribute it cannot decode. A store whose
/// `float16` setting is unreadable must not open as `float16 = false` (or with
/// any other default in place of a setting it has): it is an error.
#[test]
fn unreadable_meta_attribute_fails_open_instead_of_defaulting() {
let dir = TempDir::new().unwrap();
let path = dir.path().join("store.h5");
{
let mut m = HDF5Memory::create(config(&dir, "store.h5", true)).unwrap();
m.save(entry(1)).unwrap();
m.flush_wal().unwrap();
}
assert!(HDF5Memory::open_read_only(&path).is_ok());
// Give the `float16` attribute message an unknown version (the name is
// at +8 in a version-1 message and +9 in a version-3 one).
let mut bytes = std::fs::read(&path).unwrap();
let name = b"float16\0";
let mut hit = false;
let positions: Vec<usize> = (9..bytes.len() - name.len())
.filter(|&p| &bytes[p..p + name.len()] == name)
.collect();
for pos in positions {
for (back, version) in [(8, 1u8), (9, 3u8)] {
if bytes[pos - back] == version {
bytes[pos - back] = 0x7f;
hit = true;
}
}
}
assert!(hit, "float16 attribute message not found");
std::fs::write(&path, &bytes).unwrap();
match HDF5Memory::open_read_only(&path) {
Err(MemoryError::Schema(msg)) => assert!(msg.contains("/meta"), "{msg}"),
Err(e) => panic!("unexpected error: {e}"),
Ok(_) => panic!("store opened with an unreadable float16 setting"),
}
}
crates/clawhdf5-agent/tests/h5py_interop.rs
-155
View File
@@ -1,155 +0,0 @@
//! An agent store is a standard HDF5 file: h5py can open it and read every
//! dataset.
//!
//! It could not: the float datatype's sign-bit position was hard-coded for
//! f64, so every f32 dataset (embeddings, norms, activation weights) made
//! libhdf5 refuse the file with "sign bit position out of bounds".
use std::process::Command;
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry};
fn python() -> String {
std::env::var("CLAWHDF5_PYTHON").unwrap_or_else(|_| "python3".to_string())
}
fn h5py_available() -> bool {
Command::new(python())
.args(["-c", "import h5py"])
.output()
.map(|o| o.status.success())
.unwrap_or(false)
}
#[test]
fn h5py_reads_every_dataset_of_an_agent_store() {
if !h5py_available() {
assert!(
std::env::var("CLAWHDF5_REQUIRE_INTEROP").as_deref() != Ok("1"),
"CLAWHDF5_REQUIRE_INTEROP=1 but python3 with h5py is not available"
);
eprintln!("SKIP: python3 with h5py not available");
return;
}
let dir = tempfile::tempdir().unwrap();
for float16 in [false, true] {
let path = dir.path().join(format!("store_{float16}.h5"));
let mut cfg = MemoryConfig::new(path.clone(), "agent", 8);
cfg.float16 = float16;
let mut m = HDF5Memory::create(cfg).unwrap();
// save_batch checkpoints, so the records are in the .h5, not the WAL.
m.save_batch(
(0..20)
.map(|i| MemoryEntry {
chunk: format!("memory {i}"),
embedding: (0..8).map(|j| ((i * 8 + j) as f32).sin()).collect(),
source_channel: "test".into(),
timestamp: i as f64,
session_id: "s".into(),
tags: String::new(),
})
.collect(),
)
.unwrap();
drop(m);
// Exact expected values, as bits: numpy's sin need not match Rust's
// to the last place.
let bits = (0..160)
.map(|k| (k as f32).sin().to_bits().to_string())
.collect::<Vec<_>>()
.join(",");
let script = format!(
r#"
import h5py, numpy as np
want = np.float16 if {py_bool} else np.float32
with h5py.File("{path}", "r") as f:
names = []
f.visititems(lambda n, o: names.append(n) if isinstance(o, h5py.Dataset) else None)
for n in names:
f[n][()] # every dataset must decode
e = f["memory/embeddings"]
assert e.dtype == want, e.dtype
assert e.shape == (20, 8), e.shape
ref = np.array([{bits}], dtype=np.uint32).view(np.float32).astype(want).reshape(20, 8)
assert (e[()] == ref).all()
assert f["memory/norms"].dtype == np.float32
print(len(names))
"#,
py_bool = if float16 { "True" } else { "False" },
path = path.display()
);
let out = Command::new(python())
.args(["-c", &script])
.output()
.unwrap();
assert!(
out.status.success(),
"float16={float16}: {}",
String::from_utf8_lossy(&out.stderr)
);
let n: usize = String::from_utf8_lossy(&out.stdout).trim().parse().unwrap();
assert!(n >= 10, "only {n} datasets");
}
}
#[test]
fn an_edit_made_with_h5py_breaks_the_signature_and_names_the_record() {
if !h5py_available() {
assert!(
std::env::var("CLAWHDF5_REQUIRE_INTEROP").as_deref() != Ok("1"),
"CLAWHDF5_REQUIRE_INTEROP=1 but python3 with h5py is not available"
);
eprintln!("SKIP: python3 with h5py not available");
return;
}
use clawhdf5_agent::signing::SigningKey;
let dir = tempfile::tempdir().unwrap();
let path = dir.path().join("signed.h5");
let key = SigningKey::from_bytes(&[42; 32]);
let mut m = HDF5Memory::create(MemoryConfig::new(path.clone(), "agent", 8)).unwrap();
m.set_signing_key(key.clone());
m.save_batch(
(0..10)
.map(|i| MemoryEntry {
chunk: format!("memory {i}"),
embedding: (0..8).map(|j| ((i * 8 + j) as f32).cos()).collect(),
source_channel: "test".into(),
timestamp: i as f64,
session_id: "s".into(),
tags: String::new(),
})
.collect(),
)
.unwrap();
drop(m);
assert!(
HDF5Memory::verify(&path, &key.verifying_key())
.unwrap()
.is_valid()
);
// Someone edits one timestamp in place with h5py.
let script = format!(
r#"
import h5py
with h5py.File("{}", "r+") as f:
ts = f["memory/timestamps"]
ts[3] = 12345.0
"#,
path.display()
);
let out = Command::new(python())
.args(["-c", &script])
.output()
.unwrap();
assert!(
out.status.success(),
"{}",
String::from_utf8_lossy(&out.stderr)
);
let r = HDF5Memory::verify(&path, &key.verifying_key()).unwrap();
assert!(r.signature_valid && !r.is_valid(), "{r:?}");
assert_eq!(r.changed_records, vec![3]);
}
crates/clawhdf5-agent/tests/hnsw_integration.rs
-179
View File
@@ -165,182 +165,3 @@ fn save_batch_then_search_is_consistent() {
);
}
}
#[test]
fn quantized_index_matches_the_f32_index_after_re_scoring() {
// A quantised index holds approximate vectors, but the store still has the
// exact ones, so the query path re-scores the candidate pool before
// fusion. The results a caller sees should therefore be the same.
let dim = 64;
let n = 400;
let mut seed = 0x5EED_1234_5678_9ABC;
let vectors: Vec<Vec<f32>> = (0..n).map(|_| make_vector(&mut seed, dim)).collect();
let queries: Vec<Vec<f32>> = (0..20).map(|_| make_vector(&mut seed, dim)).collect();
let build = |dir: &TempDir, quantized: bool| {
let mut config = MemoryConfig::new(dir.path().join("mem.h5"), "agent", dim);
config.quantized_index = quantized;
let mut mem = HDF5Memory::create(config).unwrap();
for (i, v) in vectors.iter().enumerate() {
mem.save(entry(&format!("chunk {i}"), v.clone(), &format!("k{i}")))
.unwrap();
}
mem
};
let exact_dir = TempDir::new().unwrap();
let quant_dir = TempDir::new().unwrap();
let mut exact = build(&exact_dir, false);
let mut quantized = build(&quant_dir, true);
let k = 10;
let mut agree = 0;
for q in &queries {
let want: Vec<usize> = exact
.hybrid_search(q, "", 1.0, 0.0, k)
.iter()
.map(|r| r.index)
.collect();
agree += quantized
.hybrid_search(q, "", 1.0, 0.0, k)
.iter()
.filter(|r| want.contains(&r.index))
.count();
}
let overlap = agree as f64 / (k * queries.len()) as f64;
assert!(
overlap >= 0.95,
"quantised store should match the f32 one: {overlap}"
);
}
#[test]
fn quantized_index_setting_survives_a_reopen() {
let dir = TempDir::new().unwrap();
let path = dir.path().join("mem.h5");
let mut config = MemoryConfig::new(path.clone(), "agent", 8);
config.quantized_index = true;
let mut mem = HDF5Memory::create(config).unwrap();
let mut seed = 7;
for i in 0..30 {
mem.save(entry(&format!("c{i}"), make_vector(&mut seed, 8), "t"))
.unwrap();
}
mem.flush_wal().unwrap();
drop(mem);
// Reopening must not silently quadruple the index's memory, so the flag
// is part of the stored config rather than a per-session choice.
let reopened = HDF5Memory::open(&path).unwrap();
assert!(reopened.config().quantized_index);
}
#[test]
fn hnsw_parameters_are_configurable_and_persisted() {
// The graph degree and both candidate-list sizes used to be constants, so
// a deployment could not trade recall against memory or speed at all.
let dir = TempDir::new().unwrap();
let path = dir.path().join("mem.h5");
let mut config = MemoryConfig::new(path.clone(), "agent", 16);
config.hnsw_m = 8;
config.hnsw_ef_construction = 32;
config.hnsw_ef_search = 128;
let mut mem = HDF5Memory::create(config).unwrap();
let mut seed = 99;
let vectors: Vec<Vec<f32>> = (0..300).map(|_| make_vector(&mut seed, 16)).collect();
for (i, v) in vectors.iter().enumerate() {
mem.save(entry(&format!("c{i}"), v.clone(), "t")).unwrap();
}
// Still correct with a smaller graph: an exact match must rank first.
let top = mem.hybrid_search(&vectors[42], "", 1.0, 0.0, 1);
assert_eq!(top[0].index, 42);
mem.flush_wal().unwrap();
drop(mem);
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.config().hnsw_m, 8);
assert_eq!(reopened.config().hnsw_ef_construction, 32);
assert_eq!(reopened.config().hnsw_ef_search, 128);
}
#[test]
fn degenerate_hnsw_parameters_do_not_panic() {
// `clawhdf5-ann` asserts m >= 2, so a zero from a config file — or from a
// caller who assumed 0 meant "default" — would abort the process inside
// the index builder. The store clamps instead.
let dir = TempDir::new().unwrap();
let mut config = MemoryConfig::new(dir.path().join("mem.h5"), "agent", 8);
config.hnsw_m = 0;
config.hnsw_ef_construction = 0;
config.hnsw_ef_search = 1;
let mut mem = HDF5Memory::create(config).unwrap();
let mut seed = 5;
let vectors: Vec<Vec<f32>> = (0..50).map(|_| make_vector(&mut seed, 8)).collect();
for (i, v) in vectors.iter().enumerate() {
mem.save(entry(&format!("c{i}"), v.clone(), "t")).unwrap();
}
let results = mem.hybrid_search(&vectors[7], "", 1.0, 0.0, 5);
assert_eq!(results[0].index, 7, "exact match should still rank first");
}
#[test]
fn new_stores_default_to_the_quantized_index() {
// int8 is the default because it is smaller and, with an exact re-score,
// faster at equal recall on every platform measured (see BENCHMARKS.md).
let dir = TempDir::new().unwrap();
let config = MemoryConfig::new(dir.path().join("mem.h5"), "agent", 8);
assert!(config.quantized_index);
let path = config.path.clone();
let mut mem = HDF5Memory::create(config).unwrap();
let mut seed = 3;
let vectors: Vec<Vec<f32>> = (0..40).map(|_| make_vector(&mut seed, 8)).collect();
for (i, v) in vectors.iter().enumerate() {
mem.save(entry(&format!("c{i}"), v.clone(), "t")).unwrap();
}
assert_eq!(
mem.hybrid_search(&vectors[11], "", 1.0, 0.0, 1)[0].index,
11
);
mem.flush_wal().unwrap();
drop(mem);
assert!(HDF5Memory::open(&path).unwrap().config().quantized_index);
}
#[test]
fn a_store_written_before_the_setting_existed_stays_f32() {
// `store_v2_5_0.h5` was written by the v2.5.0 CLI, before
// `quantized_index` or the HNSW parameters were persisted, so it carries
// none of them. Flipping the default for new stores must not reach back
// and change how an existing store's index is held.
let dir = TempDir::new().unwrap();
let path = dir.path().join("legacy.h5");
std::fs::copy(
concat!(
env!("CARGO_MANIFEST_DIR"),
"/tests/fixtures/store_v2_5_0.h5"
),
&path,
)
.unwrap();
let bytes = std::fs::read(&path).unwrap();
assert!(
!bytes.windows(15).any(|w| w == b"quantized_index"),
"the fixture must predate the setting, or it tests nothing"
);
let mut mem = HDF5Memory::open(&path).unwrap();
assert!(
!mem.config().quantized_index,
"an old store must reopen with an f32 index"
);
assert_eq!(mem.config().hnsw_m, 16);
assert_eq!(mem.config().hnsw_ef_construction, 64);
assert_eq!(mem.count(), 6);
// And it still searches: entry 3's own embedding finds it first.
let hit = mem.hybrid_search(&[3.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "", 1.0, 0.0, 1);
assert_eq!(hit[0].index, 3);
}
crates/clawhdf5-agent/tests/memx_comparison.rs
+3 -3
View File
@@ -137,12 +137,12 @@ fn bench_hit_at_1_1014_records() {
0.3,
1,
);
if let Some((top_idx, _)) = results.first()
&& *top_idx == target_indices[qi]
{
if let Some((top_idx, _)) = results.first() {
if *top_idx == target_indices[qi] {
hits += 1;
}
}
}
let hit_at_1 = hits as f64 / NUM_QUERIES as f64;
println!(
crates/clawhdf5-agent/tests/search_options.rs
-344
View File
@@ -1,344 +0,0 @@
//! `HDF5Memory::search` with `SearchOptions`: source filtering, re-ranking and
//! confidence rejection in the store's own search path.
use std::collections::HashSet;
use clawhdf5_agent::confidence::ConfidenceConfig;
use clawhdf5_agent::reranker::ReRankConfig;
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, SearchOptions, hybrid};
use tempfile::TempDir;
const DIM: usize = 32;
const N: usize = 3000;
const CLUSTERS: usize = 20;
struct Rng(u64);
impl Rng {
fn next(&mut self) -> u64 {
self.0 = self.0.wrapping_add(0x9E37_79B9_7F4A_7C15);
let mut z = self.0;
z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
z ^ (z >> 31)
}
fn unit(&mut self) -> f32 {
(self.next() >> 40) as f32 / (1u64 << 24) as f32 - 0.5
}
}
fn normalize(v: &mut [f32]) {
let n = v.iter().map(|x| x * x).sum::<f32>().sqrt();
v.iter_mut().for_each(|x| *x /= n);
}
struct Data {
vectors: Vec<Vec<f32>>,
cluster: Vec<usize>,
centres: Vec<Vec<f32>>,
}
fn data() -> Data {
let mut rng = Rng(42);
let centres: Vec<Vec<f32>> = (0..CLUSTERS)
.map(|_| {
let mut c: Vec<f32> = (0..DIM).map(|_| rng.unit()).collect();
normalize(&mut c);
c
})
.collect();
let mut vectors = Vec::new();
let mut cluster = Vec::new();
for i in 0..N {
let c = i % CLUSTERS;
let mut v: Vec<f32> = centres[c].iter().map(|x| x + rng.unit() * 0.3).collect();
normalize(&mut v);
vectors.push(v);
cluster.push(c);
}
Data {
vectors,
cluster,
centres,
}
}
/// Channel of record `i` for a filter keeping `percent`% of the store at
/// random (independent of the vectors).
fn random_channel(i: usize, rng_seed: u64, percent: u64) -> String {
let mut r = Rng(rng_seed ^ (i as u64 * 7919));
if r.next() % 100 < percent {
"keep".into()
} else {
"other".into()
}
}
fn build(data: &Data, channel: impl Fn(usize) -> String) -> (TempDir, HDF5Memory) {
let dir = TempDir::new().unwrap();
let mut cfg = MemoryConfig::new(dir.path().join("s.h5"), "agent", DIM);
cfg.hebbian_boost = 0.0; // every query sees the same store
let mut m = HDF5Memory::create(cfg).unwrap();
let entries = data
.vectors
.iter()
.enumerate()
.map(|(i, v)| MemoryEntry {
chunk: format!("record {i} cluster {}", data.cluster[i]),
embedding: v.clone(),
source_channel: channel(i),
timestamp: i as f64,
session_id: "s".into(),
tags: format!("t{i}"),
})
.collect();
m.save_batch(entries).unwrap();
(dir, m)
}
/// Exact top-k by cosine among the records `allowed` keeps.
fn exact_top(data: &Data, q: &[f32], k: usize, allowed: impl Fn(usize) -> bool) -> Vec<usize> {
let mut s: Vec<(usize, f32)> = (0..N)
.filter(|&i| allowed(i))
.map(|i| (i, data.vectors[i].iter().zip(q).map(|(a, b)| a * b).sum()))
.collect();
s.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
s.into_iter().take(k).map(|(i, _)| i).collect()
}
fn query(data: &Data, i: usize) -> Vec<f32> {
let mut rng = Rng(1000 + i as u64);
let mut q: Vec<f32> = data.centres[i % CLUSTERS]
.iter()
.map(|x| x + rng.unit() * 0.3)
.collect();
normalize(&mut q);
q
}
fn vector_only(k: usize) -> SearchOptions {
SearchOptions::new(k).with_fusion(hybrid::Fusion::Weighted {
vector: 1.0,
keyword: 0.0,
})
}
#[test]
fn source_filter_returns_only_allowed_records_and_a_full_page() {
let d = data();
// At N = 3000 and k = 10 the index serves a filter only when that is
// cheaper than scanning the allowed records: pool = 80 * N / allowed
// candidates at ~M = 16 distances each, against `allowed` distances. So
// 90% goes through the index, 50% and 1% to the exact scan.
for percent in [90, 50, 1] {
let (_dir, mut m) = build(&d, |i| random_channel(i, 5, percent));
let allowed = |i: usize| random_channel(i, 5, percent) == "keep";
let mut hits = 0;
for qi in 0..40 {
let q = query(&d, qi);
let got = m.search(&q, "", &vector_only(10).with_sources(["keep"]));
assert_eq!(got.len(), 10, "{percent}%: short page");
assert!(got.iter().all(|r| r.source_channel == "keep"));
let want: HashSet<usize> = exact_top(&d, &q, 10, allowed).into_iter().collect();
hits += got.iter().filter(|r| want.contains(&r.index)).count();
}
let recall = hits as f64 / 400.0;
let floor = if percent == 90 { 0.95 } else { 1.0 };
assert!(recall >= floor, "{percent}%: recall@10 {recall}");
}
}
#[test]
fn filter_away_from_the_query_falls_back_to_an_exact_scan() {
// Channel = cluster, and the filter keeps two clusters (10% of the
// store) that are not the query's: the index's neighbourhood of the
// query holds none of them. The search must still return the exact
// top 10 among the allowed records, not a short or empty page.
let d = data();
let (_dir, mut m) = build(&d, |i| format!("c{}", d.cluster[i]));
for qi in 0..20 {
let q = query(&d, qi);
let a = format!("c{}", (qi + 7) % CLUSTERS);
let b = format!("c{}", (qi + 13) % CLUSTERS);
let got: Vec<usize> = m
.search(
&q,
"",
&vector_only(10).with_sources([a.clone(), b.clone()]),
)
.iter()
.map(|r| r.index)
.collect();
let want = exact_top(&d, &q, 10, |i| {
let c = format!("c{}", d.cluster[i]);
c == a || c == b
});
assert_eq!(got, want, "query {qi}");
}
}
#[test]
fn filter_edge_cases() {
let d = data();
let (_dir, mut m) = build(&d, |i| random_channel(i, 9, 50));
let q = query(&d, 0);
assert!(
m.search(
&q,
"cluster",
&SearchOptions::new(10).with_sources(Vec::<String>::new())
)
.is_empty()
);
assert!(
m.search(
&q,
"cluster",
&SearchOptions::new(10).with_sources(["nope"])
)
.is_empty()
);
// Keyword matches from other channels are filtered too.
let got = m.search(
&q,
"record cluster",
&SearchOptions::new(50).with_sources(["keep"]),
);
assert_eq!(got.len(), 50);
assert!(got.iter().all(|r| r.source_channel == "keep"));
// Deleted records never come back, filtered or not.
let first = got[0].index;
m.delete(first).unwrap();
let again = m.search(
&q,
"record cluster",
&SearchOptions::new(50).with_sources(["keep"]),
);
assert!(again.iter().all(|r| r.index != first));
}
#[test]
fn plain_options_equal_hybrid_search_with() {
// Two identical stores, so neither query sees the other's boosts.
let d = data();
let (_a, mut a) = build(&d, |i| random_channel(i, 3, 50));
let (_b, mut b) = build(&d, |i| random_channel(i, 3, 50));
for qi in 0..10 {
let q = query(&d, qi);
let x: Vec<(usize, u32)> = a
.search(&q, "record cluster 3", &SearchOptions::new(10))
.iter()
.map(|r| (r.index, r.score.to_bits()))
.collect();
let y: Vec<(usize, u32)> = b
.hybrid_search_with(&q, "record cluster 3", hybrid::DEFAULT_FUSION, 10)
.iter()
.map(|r| (r.index, r.score.to_bits()))
.collect();
assert_eq!(x, y);
}
}
fn small_store(entries: &[(&str, &str, f64)]) -> (TempDir, HDF5Memory) {
let dir = TempDir::new().unwrap();
let mut m = HDF5Memory::create(MemoryConfig::new(dir.path().join("r.h5"), "a", 4)).unwrap();
m.save_batch(
entries
.iter()
.map(|(chunk, channel, ts)| MemoryEntry {
chunk: chunk.to_string(),
embedding: vec![1.0, 0.0, 0.0, 0.0],
source_channel: channel.to_string(),
timestamp: *ts,
session_id: "s".into(),
tags: String::new(),
})
.collect(),
)
.unwrap();
(dir, m)
}
#[test]
fn rerank_breaks_relevance_ties_by_recency() {
// Identical text and vectors, so retrieval ties; re-ranking must put the
// newer record first and report the combined score.
let now = 1_000_000.0;
let (_d, mut m) = small_store(&[
("user prefers dark mode", "chat", now - 30.0 * 86_400.0),
("user prefers dark mode", "chat", now - 60.0),
]);
let q = [1.0, 0.0, 0.0, 0.0];
let plain = m.search(&q, "dark mode", &SearchOptions::new(2));
assert_eq!(plain[0].index, 0, "ties break by index without re-ranking");
let reranked = m.search(
&q,
"dark mode",
&SearchOptions::new(2)
.with_rerank(ReRankConfig::default())
.at_time(now),
);
assert_eq!(reranked[0].index, 1);
assert!(reranked[0].score > reranked[1].score);
assert_ne!(reranked[0].score.to_bits(), plain[0].score.to_bits());
}
#[test]
fn confidence_rejects_when_nothing_is_good_enough() {
let (_d, mut m) = small_store(&[("alpha", "chat", 0.0), ("beta", "chat", 0.0)]);
let q = [1.0, 0.0, 0.0, 0.0];
let strict = ConfidenceConfig {
min_score: 10.0,
..ConfidenceConfig::default()
};
assert!(
m.search(&q, "alpha", &SearchOptions::new(2).with_confidence(strict))
.is_empty()
);
let lenient = ConfidenceConfig {
min_score: 0.0,
min_gap: f32::INFINITY,
max_results: 1,
};
assert_eq!(
m.search(&q, "alpha", &SearchOptions::new(2).with_confidence(lenient))
.len(),
1
);
}
#[test]
fn only_returned_results_are_reinforced() {
// With re-ranking, a pool of max(3k, 10) candidates is retrieved; only
// the k returned should gain activation.
let d = data();
let dir = TempDir::new().unwrap();
let path = dir.path().join("h.h5");
let mut m = HDF5Memory::create(MemoryConfig::new(path, "a", DIM)).unwrap();
m.save_batch(
(0..200)
.map(|i| MemoryEntry {
chunk: format!("record {i}"),
embedding: d.vectors[i].clone(),
source_channel: "chat".into(),
timestamp: i as f64,
session_id: "s".into(),
tags: String::new(),
})
.collect(),
)
.unwrap();
let q = query(&d, 0);
let got = m.search(
&q,
"record",
&SearchOptions::new(3).with_rerank(ReRankConfig::default()),
);
assert_eq!(got.len(), 3);
let returned: HashSet<usize> = got.iter().map(|r| r.index).collect();
// A second plain search reports each record's current activation.
let all = m.search(&q, "record", &SearchOptions::new(200));
for r in &all {
let boosted = r.activation > 1.0;
assert_eq!(boosted, returned.contains(&r.index), "record {}", r.index);
}
}
crates/clawhdf5-agent/tests/signed_store.rs
-330
View File
@@ -1,330 +0,0 @@
//! Ed25519-signed checkpoints: `HDF5Memory::set_signing_key` and
//! `HDF5Memory::verify`.
use std::path::Path;
use clawhdf5_agent::signing::{SigningKey, VerifyReport, VerifyingKey};
use clawhdf5_agent::storage;
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, MemoryError, schema};
use tempfile::TempDir;
const DIM: usize = 16;
fn key(seed: u8) -> SigningKey {
SigningKey::from_bytes(&[seed; 32])
}
fn entry(i: usize, chunk: &str) -> MemoryEntry {
MemoryEntry {
chunk: chunk.to_string(),
embedding: (0..DIM)
.map(|j| ((i * DIM + j) as f32 * 0.37).sin())
.collect(),
source_channel: "chat".into(),
timestamp: 1_700_000_000.0 + i as f64,
session_id: format!("s{}", i % 3),
tags: format!("t{i}"),
}
}
/// Awkward strings on purpose: they must hash the same after a round trip.
const TEXTS: [&str; 6] = [
"plain text",
"ünïcödé — 日本語 🙂",
"",
"trailing spaces ",
"tab\tand\nnewline",
"x",
];
fn signed_store(dir: &TempDir, float16: bool, k: &SigningKey) -> std::path::PathBuf {
let mut cfg = MemoryConfig::new(dir.path().join("s.h5"), "agent", DIM);
cfg.float16 = float16;
let path = cfg.path.clone();
let mut m = HDF5Memory::create(cfg).unwrap();
m.set_signing_key(k.clone());
let entries = (0..30).map(|i| entry(i, TEXTS[i % TEXTS.len()])).collect();
m.save_batch(entries).unwrap();
// Some graph and a deleted record, so every part of the manifest is used.
let a = m.knowledge_mut().add_entity("Alice", "person", 0);
let b = m.knowledge_mut().add_entity("Acme", "org", -1);
m.knowledge_mut().add_relation(a, b, "works_at", 0.75);
m.sessions_mut()
.add_at("s0", 0, 9, "chat", "first session", 1_700_000_000.0);
m.delete(4).unwrap();
m.flush_wal().unwrap();
path
}
fn verify(path: &Path, k: &SigningKey) -> VerifyReport {
HDF5Memory::verify(path, &k.verifying_key()).unwrap()
}
#[test]
fn a_signed_store_verifies_through_reopen_and_checkpoint_cycles() {
for float16 in [true, false] {
let dir = TempDir::new().unwrap();
let k = key(7);
let path = signed_store(&dir, float16, &k);
let r = verify(&path, &k);
assert!(r.is_valid(), "float16={float16}: {r:?}");
assert_eq!(r.public_key, Some(k.verifying_key().to_bytes()));
assert_eq!(r.record_count, 30);
assert!(r.changed_records.is_empty());
// Reopen, change nothing, checkpoint again (with the key): still valid.
for _ in 0..3 {
let mut m = HDF5Memory::open(&path).unwrap();
assert!(m.is_signed());
m.set_signing_key(k.clone());
m.flush_wal().unwrap();
drop(m);
assert!(verify(&path, &k).is_valid());
}
// And after real changes, re-signed.
let mut m = HDF5Memory::open(&path).unwrap();
m.set_signing_key(k.clone());
m.save(entry(99, "added later")).unwrap();
m.hybrid_search(&entry(1, "").embedding, "text", 0.4, 0.6, 5);
m.flush_wal().unwrap();
drop(m);
let r = verify(&path, &k);
assert!(r.is_valid(), "{r:?}");
assert_eq!(r.record_count, 31);
}
}
#[test]
fn a_signed_store_refuses_to_checkpoint_without_its_key() {
let dir = TempDir::new().unwrap();
let k = key(1);
let path = signed_store(&dir, true, &k);
let mut m = HDF5Memory::open(&path).unwrap();
m.save(entry(50, "pending")).unwrap();
match m.flush_wal() {
Err(MemoryError::SigningKeyRequired(msg)) => assert!(msg.contains("signed"), "{msg}"),
other => panic!("expected SigningKeyRequired, got {other:?}"),
}
// The file is untouched and still valid; the save is still in the WAL.
let r = verify(&path, &k);
assert!(r.is_valid());
assert_eq!(r.wal_entries_unsigned, 1);
// Supplying the key lets the checkpoint through, signed.
m.set_signing_key(k.clone());
m.flush_wal().unwrap();
drop(m);
let r = verify(&path, &k);
assert!(r.is_valid());
assert_eq!((r.record_count, r.wal_entries_unsigned), (31, 0));
// Removing the signature on purpose writes it unsigned.
let mut m = HDF5Memory::open(&path).unwrap();
m.remove_signature();
m.flush_wal().unwrap();
drop(m);
let r = verify(&path, &k);
assert!(!r.signed && !r.is_valid());
assert!(!HDF5Memory::open(&path).unwrap().is_signed());
}
#[test]
fn the_wrong_key_does_not_verify_and_a_new_key_re_signs() {
let dir = TempDir::new().unwrap();
let (a, b) = (key(1), key(2));
let path = signed_store(&dir, true, &a);
let r = verify(&path, &b);
assert!(r.signed && !r.key_matches && !r.signature_valid && !r.is_valid());
let mut m = HDF5Memory::open(&path).unwrap();
m.set_signing_key(b.clone());
m.flush_wal().unwrap();
drop(m);
assert!(verify(&path, &b).is_valid());
assert!(!verify(&path, &a).is_valid());
}
/// Rewrite the store with changed contents but the *old* signature — what
/// someone with write access to the file, but not the key, can do.
fn tamper(path: &Path, change: impl FnOnce(&mut Tampered)) {
let file = clawhdf5::File::open(path).unwrap();
let (config, cache, sessions, knowledge) = schema::validate_and_load(&file).unwrap();
let checkpoint = schema::read_checkpoint_meta(&file);
let signature = schema::read_signature(&file).unwrap().unwrap();
drop(file);
let mut t = Tampered {
config,
cache,
sessions,
knowledge,
};
change(&mut t);
storage::write_to_disk_signed(
path,
&t.config,
&t.cache,
&t.sessions,
&t.knowledge,
&checkpoint,
Some(&signature),
)
.unwrap();
}
struct Tampered {
config: MemoryConfig,
cache: clawhdf5_agent::cache::MemoryCache,
sessions: clawhdf5_agent::SessionCache,
knowledge: clawhdf5_agent::knowledge::KnowledgeCache,
}
#[test]
fn every_kind_of_edit_is_detected_and_located() {
let k = key(3);
type Edit = Box<dyn FnOnce(&mut Tampered)>;
type Case = (&'static str, Edit, fn(&VerifyReport) -> bool);
let cases: Vec<Case> = vec![
(
"record text",
Box::new(|t: &mut Tampered| t.cache.chunks[7] = "rewritten".into()),
|r| !r.records_match && r.changed_records == vec![7],
),
(
"one embedding value",
Box::new(|t: &mut Tampered| {
let mut e = t.cache.embeddings[12].to_vec();
e[3] = 0.5;
t.cache.embeddings.set(12, &e);
}),
|r| r.changed_records == vec![12],
),
(
"undelete",
Box::new(|t: &mut Tampered| t.cache.tombstones[4] = 0),
|r| r.changed_records == vec![4],
),
(
"timestamp",
Box::new(|t: &mut Tampered| t.cache.timestamps[20] += 1.0),
|r| r.changed_records == vec![20],
),
(
"record appended",
Box::new(|t: &mut Tampered| {
t.cache.push(
"new".into(),
vec![0.1; DIM],
"x".into(),
1.0,
"s".into(),
"".into(),
);
}),
|r| !r.records_match && r.changed_records == vec![30] && r.record_count == 31,
),
(
"setting",
Box::new(|t: &mut Tampered| t.config.agent_id = "someone-else".into()),
|r| !r.settings_match && r.records_match,
),
(
"session summary",
Box::new(|t: &mut Tampered| t.sessions.summaries[0] = "edited".into()),
|r| !r.sessions_match && r.records_match,
),
(
"graph edge",
Box::new(|t: &mut Tampered| t.knowledge.relations[0].weight = 1.0),
|r| !r.graph_match && r.records_match,
),
];
for (name, edit, check) in cases {
let dir = TempDir::new().unwrap();
let path = signed_store(&dir, true, &k);
tamper(&path, edit);
let r = verify(&path, &k);
assert!(
r.signed && r.key_matches && r.signature_valid,
"{name}: {r:?}"
);
assert!(!r.is_valid(), "{name}: edit not detected: {r:?}");
assert!(check(&r), "{name}: {r:?}");
}
}
#[test]
fn a_forged_manifest_fails_the_signature() {
// Recomputing the hashes for tampered contents does not help without the
// key: the signature no longer matches the manifest.
let dir = TempDir::new().unwrap();
let k = key(5);
let path = signed_store(&dir, true, &k);
let file = clawhdf5::File::open(&path).unwrap();
let (config, mut cache, sessions, knowledge) = schema::validate_and_load(&file).unwrap();
let checkpoint = schema::read_checkpoint_meta(&file);
let mut sig = schema::read_signature(&file).unwrap().unwrap();
drop(file);
cache.chunks[0] = "forged".into();
// Re-sign with an attacker key, then splice the victim's public key back.
let forged = clawhdf5_agent::signing::sign(
&key(66),
&config,
&cache,
&sessions,
&knowledge,
checkpoint.wal_applied,
);
sig.manifest = forged.manifest;
sig.record_hashes = forged.record_hashes;
storage::write_to_disk_signed(
&path,
&config,
&cache,
&sessions,
&knowledge,
&checkpoint,
Some(&sig),
)
.unwrap();
let r = verify(&path, &k);
assert!(
r.key_matches && !r.signature_valid && !r.is_valid(),
"{r:?}"
);
}
#[test]
fn an_unsigned_store_reports_unsigned() {
let dir = TempDir::new().unwrap();
let mut m = HDF5Memory::create(MemoryConfig::new(dir.path().join("u.h5"), "a", DIM)).unwrap();
m.save_batch(vec![entry(0, "hello")]).unwrap();
drop(m);
let r = HDF5Memory::verify(&dir.path().join("u.h5"), &VerifyingKey::from(&key(1))).unwrap();
assert!(!r.signed && !r.is_valid());
assert_eq!(r.record_count, 1);
}
#[test]
fn nul_bytes_in_text_still_verify() {
// Strings are stored null-padded; the hash must follow what a reopened
// store actually holds, or an untouched store would fail to verify.
let dir = TempDir::new().unwrap();
let k = key(9);
let mut m = HDF5Memory::create(MemoryConfig::new(dir.path().join("n.h5"), "a", DIM)).unwrap();
m.set_signing_key(k.clone());
m.save_batch(vec![
entry(0, "inner\0nul"),
entry(1, "trailing nul\0"),
entry(2, "\0leading"),
])
.unwrap();
drop(m);
let r = verify(&dir.path().join("n.h5"), &k);
assert!(r.is_valid(), "{r:?}");
let m = HDF5Memory::open(&dir.path().join("n.h5")).unwrap();
eprintln!(
"reloaded: {:?}",
(0..3).map(|i| m.get_chunk(i)).collect::<Vec<_>>()
);
}
crates/clawhdf5-agent/tests/stress_tests.rs
+5 -5
View File
@@ -105,7 +105,7 @@ fn test_heavy_tombstoning() {
assert_eq!(mem.count_active(), 5000);
// Verify persistence
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 5000);
}
@@ -163,7 +163,7 @@ fn test_large_embeddings_1536() {
assert_eq!(mem.count(), 10_000);
// Verify persistence
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 10_000);
// Verify search works on large dims
@@ -545,7 +545,7 @@ fn test_delete_all_entries() {
assert_eq!(mem.count(), 0);
// Verify persistence
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 0);
}
@@ -639,7 +639,7 @@ fn test_unicode_content() {
];
mem.save_batch(entries).unwrap();
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 3);
let (_, cache, _, _) = clawhdf5_agent::storage::read_from_disk(&path).unwrap();
@@ -685,6 +685,6 @@ fn test_rapid_save_delete_cycles() {
assert_eq!(removed, 250);
assert_eq!(mem.count(), 250);
let reopened = HDF5Memory::open_read_only(&path).unwrap();
let reopened = HDF5Memory::open(&path).unwrap();
assert_eq!(reopened.count(), 250);
}
crates/clawhdf5-agent/tests/wal_properties.rs
-213
View File
@@ -1,213 +0,0 @@
//! Property tests for the write-ahead log.
//!
//! A deterministic generator (no external crates, reproducible from the seed
//! printed on failure) drives thousands of cases through two properties:
//!
//! 1. **Round trip** — whatever was appended is read back, in order, intact.
//! 2. **Prefix under corruption** — after *any* damage to the file (bit flips,
//! truncation, inserted or deleted bytes, duplicated or reordered regions),
//! reading never panics and yields an exact *prefix* of what was written.
//! This is the guarantee the chained CRC exists to provide: replay may stop
//! early, but it never returns a corrupted, reordered, or invented entry.
use clawhdf5_agent::wal::{WalEntry, WalEntryType, WalFile};
/// SplitMix64: tiny, well-distributed, and fully determined by its seed.
struct Rng(u64);
impl Rng {
fn next(&mut self) -> u64 {
self.0 = self.0.wrapping_add(0x9E37_79B9_7F4A_7C15);
let mut z = self.0;
z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
z ^ (z >> 31)
}
fn below(&mut self, n: usize) -> usize {
(self.next() % n.max(1) as u64) as usize
}
fn string(&mut self, max_len: usize) -> String {
const ALPHABET: &[char] = &['a', 'Z', '0', ' ', '\n', '\0', 'é', '漢', '🦀', '"'];
(0..self.below(max_len + 1))
.map(|_| ALPHABET[self.below(ALPHABET.len())])
.collect()
}
}
/// What a test appended, in a form comparable with what is read back.
#[derive(Debug, Clone, PartialEq)]
enum Logged {
Save(String, Vec<u32>, String, String, String, u64),
Update(usize, String, Vec<u32>, u64),
Tombstone(usize, u64),
}
fn logged(entry: &WalEntry) -> Logged {
// Compare floats by bit pattern so NaN payloads and -0.0 count as intact.
let bits: Vec<u32> = entry.embedding.iter().map(|f| f.to_bits()).collect();
let ts = entry.timestamp.to_bits();
match entry.entry_type {
WalEntryType::Save => Logged::Save(
entry.chunk.clone(),
bits,
entry.source_channel.clone(),
entry.session_id.clone(),
entry.tags.clone(),
ts,
),
WalEntryType::Update => {
Logged::Update(entry.update_index.unwrap(), entry.chunk.clone(), bits, ts)
}
WalEntryType::Tombstone => Logged::Tombstone(entry.tombstone_index.unwrap(), ts),
WalEntryType::ActivationUpdate => unreachable!("never written by these tests"),
}
}
/// Append a random mix of records; return what was written.
fn write_random_wal(path: &std::path::Path, rng: &mut Rng) -> Vec<Logged> {
let mut wal = WalFile::open(path).unwrap();
let mut written = Vec::new();
for _ in 0..rng.below(12) {
let timestamp = f64::from_bits(rng.next());
if rng.below(5) == 0 {
let index = rng.below(1000);
wal.append_tombstone(index, timestamp).unwrap();
written.push(Logged::Tombstone(index, timestamp.to_bits()));
continue;
}
let update_index = (rng.below(4) == 0).then(|| rng.below(1000));
let entry = WalEntry {
entry_type: if update_index.is_some() {
WalEntryType::Update
} else {
WalEntryType::Save
},
timestamp,
chunk: rng.string(40),
embedding: (0..rng.below(9))
.map(|_| f32::from_bits(rng.next() as u32))
.collect(),
source_channel: rng.string(8),
session_id: rng.string(8),
tags: rng.string(8),
tombstone_index: None,
update_index,
};
wal.append_save(&entry).unwrap();
written.push(logged(&entry));
}
written
}
fn read_back(path: &std::path::Path) -> Option<Vec<Logged>> {
WalFile::read_entries(path)
.ok()
.map(|entries| entries.iter().map(logged).collect())
}
#[test]
fn everything_appended_is_read_back_intact() {
let dir = tempfile::TempDir::new().unwrap();
for seed in 0..300u64 {
let path = dir.path().join(format!("rt-{seed}.wal"));
let written = write_random_wal(&path, &mut Rng(seed));
assert_eq!(read_back(&path).unwrap(), written, "seed {seed}");
// Reopening (which scans and repositions) must not disturb anything.
drop(WalFile::open(&path).unwrap());
assert_eq!(
read_back(&path).unwrap(),
written,
"seed {seed} after reopen"
);
}
}
/// Damage `bytes` in one of several ways.
fn corrupt(bytes: &mut Vec<u8>, rng: &mut Rng) {
if bytes.is_empty() {
return;
}
match rng.below(7) {
0 => {
let i = rng.below(bytes.len());
bytes[i] ^= 1 << rng.below(8);
}
1 => bytes.truncate(rng.below(bytes.len())),
2 => {
let i = rng.below(bytes.len() + 1);
bytes.insert(i, rng.next() as u8);
}
3 => {
let i = rng.below(bytes.len());
bytes.remove(i);
}
4 => {
// Duplicate a region in place (a replayed/duplicated entry).
let a = rng.below(bytes.len());
let b = a + rng.below(bytes.len() - a);
let region = bytes[a..b].to_vec();
let at = rng.below(bytes.len() + 1);
bytes.splice(at..at, region);
}
5 => {
// Swap two regions (reordered entries).
let mid = rng.below(bytes.len());
bytes.rotate_left(mid);
}
_ => {
let i = rng.below(bytes.len());
let n = rng.below(bytes.len() - i + 1);
for b in &mut bytes[i..i + n] {
*b = rng.next() as u8;
}
}
}
}
#[test]
fn any_corruption_yields_a_prefix_never_a_wrong_entry() {
let dir = tempfile::TempDir::new().unwrap();
let mut shortened = 0u32;
for seed in 0..1500u64 {
let mut rng = Rng(seed ^ 0xC0FF_EE00);
let path = dir.path().join("c.wal");
let _ = std::fs::remove_file(&path);
let written = write_random_wal(&path, &mut rng);
let mut bytes = std::fs::read(&path).unwrap();
for _ in 0..=rng.below(3) {
corrupt(&mut bytes, &mut rng);
}
std::fs::write(&path, &bytes).unwrap();
// An unreadable header is a clean error; anything else is a prefix.
if let Some(read) = read_back(&path) {
assert!(
read.len() <= written.len() && read[..] == written[..read.len()],
"seed {seed}: read {read:?}\nis not a prefix of {written:?}"
);
if read.len() < written.len() {
shortened += 1;
}
// Opening for append repairs the tail; what was readable stays so,
// and a new entry lands right after it.
if let Ok(mut wal) = WalFile::open(&path) {
wal.append_tombstone(7, 1.0).unwrap();
drop(wal);
let mut expected = read.clone();
expected.push(Logged::Tombstone(7, 1.0f64.to_bits()));
assert_eq!(
read_back(&path).unwrap(),
expected,
"seed {seed} after repair"
);
}
}
}
assert!(
shortened > 100,
"corruption rarely took effect: {shortened}"
);
}
crates/clawhdf5-android/Cargo.toml
+1 -2
View File
@@ -1,8 +1,7 @@
[package]
name = "clawhdf5-android"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "Android JNI bridge for edgehdf5-memory HDF5 backend"
license = "MIT"
crates/clawhdf5-ann/Cargo.toml
+4 -6
View File
@@ -1,19 +1,17 @@
[package]
name = "clawhdf5-ann"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "HNSW approximate nearest neighbor index stored as HDF5"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
readme = "README.md"
keywords = ["hdf5", "ann", "hnsw", "nearest-neighbor"]
categories = ["algorithms", "science"]
[dependencies]
clawhdf5-format = { path = "../clawhdf5-format", version = "2.7.0" }
clawhdf5-io = { path = "../clawhdf5-io", version = "2.7.0" }
clawhdf5-accel = { path = "../clawhdf5-accel", version = "2.7.0" }
clawhdf5-format = { path = "../clawhdf5-format", version = "2.1.0" }
clawhdf5-io = { path = "../clawhdf5-io", version = "2.1.0" }
rayon = { version = "1", optional = true }
[features]
crates/clawhdf5-ann/src/hnsw.rs
+149 -1120
View File
File diff suppressed because it is too large Load Diff
crates/clawhdf5-ann/src/lib.rs
+1 -1
View File
@@ -5,4 +5,4 @@
mod hnsw;
pub use hnsw::{DistanceMetric, HnswIndex, Storage};
pub use hnsw::{DistanceMetric, HnswIndex};
crates/clawhdf5-bench/Cargo.toml
+1 -13
View File
@@ -1,8 +1,7 @@
[package]
name = "clawhdf5-bench"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "Benchmark harnesses for clawhdf5-agent (Track 8)"
license = "MIT"
@@ -14,14 +13,6 @@ path = "src/bin/longmemeval_bench.rs"
name = "memory_arena"
path = "src/bin/memory_arena.rs"
[[bin]]
name = "read_harness"
path = "src/bin/read_harness.rs"
[[bin]]
name = "search_harness"
path = "src/bin/search_harness.rs"
[[bin]]
name = "footprint_bench"
path = "src/bin/footprint_bench.rs"
@@ -57,9 +48,6 @@ harness = false
[dependencies]
clawhdf5-agent = { path = "../clawhdf5-agent" }
clawhdf5-ann = { path = "../clawhdf5-ann" }
clawhdf5 = { path = "../clawhdf5" }
clawhdf5-format = { path = "../clawhdf5-format" }
clawhdf5-io = { path = "../clawhdf5-io" }
mpi = { version = "0.8", optional = true }
serde = { workspace = true }
crates/clawhdf5-bench/src/bin/consolidation_efficiency.rs
+10 -18
View File
@@ -22,9 +22,7 @@
use std::time::Instant;
use clawhdf5_agent::bm25::BM25Index;
use clawhdf5_agent::consolidation::{
ConsolidationConfig, ConsolidationEngine, TrustedSource, UntrustedSource,
};
use clawhdf5_agent::consolidation::{ConsolidationConfig, ConsolidationEngine, MemorySource};
use clawhdf5_agent::hybrid::hybrid_search;
const EMBEDDING_DIM: usize = 384;
@@ -234,7 +232,7 @@ fn run_quality_benchmark() {
for i in 0..SIGNAL_KEYWORDS.len() {
let chunk = make_signal_content(i);
let embedding = make_embedding(i * 1000);
let id = engine.add_trusted_memory(chunk, embedding, TrustedSource::Correction, now);
let id = engine.add_memory(chunk, embedding, MemorySource::Correction, now);
signal_ids.push(id);
}
@@ -242,12 +240,7 @@ fn run_quality_benchmark() {
for i in 0..990 {
let chunk = make_noise_content(i);
let embedding = make_embedding(i + 100);
engine.add_trusted_memory(
chunk,
embedding,
TrustedSource::System,
now + i as f64 * 0.1,
);
engine.add_memory(chunk, embedding, MemorySource::System, now + i as f64 * 0.1);
}
println!(" → Inserted {} records total", engine.records().len());
@@ -340,7 +333,7 @@ fn run_cycle_time_benchmark() {
for i in 0..n {
let chunk = make_noise_content(i);
let embedding = make_embedding(i);
engine.add_memory(chunk, embedding, UntrustedSource::User, now + i as f64);
engine.add_memory(chunk, embedding, MemorySource::User, now + i as f64);
}
// Warmup
@@ -351,7 +344,7 @@ fn run_cycle_time_benchmark() {
for i in n..(n * 2) {
let chunk = make_noise_content(i);
let embedding = make_embedding(i);
engine.add_memory(chunk, embedding, UntrustedSource::User, now + i as f64);
engine.add_memory(chunk, embedding, MemorySource::User, now + i as f64);
}
// Timed consolidation
@@ -396,7 +389,7 @@ fn run_memory_reduction_benchmark() {
println!();
println!(
"{:>8} {:>10} {:>10} {:>10} {:>12}",
"Initial", "Remaining", "Eviction%", "Signal OK?", "Records ÷"
"Initial", "Remaining", "Eviction%", "Signal OK?", "BM25 Speedup"
);
println!("{}", "-".repeat(58));
@@ -417,13 +410,13 @@ fn run_memory_reduction_benchmark() {
for i in 0..signal_count {
let chunk = make_signal_content(i % SIGNAL_KEYWORDS.len());
let emb = make_embedding(i * 999);
let id = engine.add_trusted_memory(chunk, emb, TrustedSource::Correction, now);
let id = engine.add_memory(chunk, emb, MemorySource::Correction, now);
signal_ids.push(id);
}
for i in 0..noise_count {
let chunk = make_noise_content(i);
let emb = make_embedding(i + 200);
engine.add_trusted_memory(chunk, emb, TrustedSource::System, now + i as f64 * 0.1);
engine.add_memory(chunk, emb, MemorySource::System, now + i as f64 * 0.1);
}
// Access signal records heavily
@@ -440,8 +433,7 @@ fn run_memory_reduction_benchmark() {
// Check all signal records survived
let signal_survived = signal_ids.iter().all(|&id| engine.get_by_id(id).is_some());
// How many times fewer records there are. Not a measured speedup —
// Part 1 measures search latency before and after.
// Rough speedup: BM25 scales roughly linearly with record count
let speedup = before_count as f64 / after_count.max(1) as f64;
println!(
@@ -481,7 +473,7 @@ fn main() {
println!(" 3. Reducing search latency proportional to record reduction");
println!();
println!(
"Cycle time grows a little faster than linearly: 100 records ~microseconds, 100K records ~tens of ms."
"Cycle time scales sub-linearly: 100 records ~microseconds, 100K records ~tens of ms."
);
println!("Signal records with Correction source + high access_count survive eviction.");
}
crates/clawhdf5-bench/src/bin/footprint_bench.rs
+3 -19
View File
@@ -11,14 +11,12 @@
//!
//! Configuration matrix:
//! - Text lengths: short (50 chars), medium (200 chars), long (1000 chars)
//! - Embedding: 384-dim, stored as float16 (the default for new stores) or
//! f32 with `--f32`; "raw" bytes are counted as f32 input either way
//! - Embedding: 384-dim f32 (1536 bytes raw per record)
//! - WAL: enabled and disabled
//!
//! # Usage
//! ```
//! cargo run --release --bin footprint_bench # float16 stores
//! cargo run --release --bin footprint_bench -- --f32 # f32 stores
//! cargo run --release --bin footprint_bench
//! ```
use std::time::Instant;
@@ -26,9 +24,6 @@ use std::time::Instant;
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry};
use tempfile::TempDir;
/// `--f32`: build f32 stores instead of the library's float16 default.
static F32: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
const EMBEDDING_DIM: usize = 384;
// Raw bytes per record: 384 f32 embeddings + median text + overhead
@@ -157,9 +152,6 @@ fn measure_footprint(
config.compression = compression;
config.compression_level = if compression { 6 } else { 0 };
config.compact_threshold = 0.0;
if F32.load(std::sync::atomic::Ordering::Relaxed) {
config.float16 = false;
}
let mut memory = HDF5Memory::create(config).expect("HDF5Memory::create failed");
@@ -249,19 +241,11 @@ fn fmt_n(n: usize) -> String {
// ---------------------------------------------------------------------------
fn main() {
if std::env::args().skip(1).any(|a| a == "--f32") {
F32.store(true, std::sync::atomic::Ordering::Relaxed);
}
let stored = if F32.load(std::sync::atomic::Ordering::Relaxed) {
"f32 (1,536 bytes per record)"
} else {
"float16 (768 bytes per record; the default for new stores)"
};
println!("=================================================================");
println!(" ClawhDF5 Memory Footprint Benchmark");
println!("=================================================================");
println!();
println!("Embedding: 384-dim, stored as {stored}; raw input counted as f32");
println!("Embedding: 384-dim f32 = 1,536 bytes raw per record");
println!("Text lengths: short=50 chars, medium=200 chars, long=1000 chars");
println!();
crates/clawhdf5-bench/src/bin/longmemeval_bench.rs
+44 -376
View File
@@ -55,155 +55,44 @@ use std::time::{Duration, Instant};
#[path = "longmemeval_bench/embedder.rs"]
mod embedder;
use clawhdf5_agent::bm25::TokenFilter;
use clawhdf5_agent::hybrid::Fusion;
use clawhdf5_agent::reranker::{ReRankConfig, RerankInput, rerank};
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, SearchResult};
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry};
use serde::Deserialize;
use tempfile::TempDir;
const EMBEDDING_DIM: usize = 384;
/// `--float16`: build every per-question store with `MemoryConfig::float16`,
/// so embeddings are rounded to half precision as they are saved — exactly
/// what such a store searches over.
static FLOAT16: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
/// A mode's fusion, as one short string for the reports.
fn describe(mode: Mode) -> String {
let fusion = match mode.fusion {
Fusion::Weighted { vector, keyword } => format!("vector_{vector:.1}_keyword_{keyword:.1}"),
Fusion::Rrf { k } => format!("rrf_k{k:.0}"),
};
let tokens = match mode.tokens {
TokenFilter::Plain => fusion,
TokenFilter::Stemmed => format!("{fusion}_stemmed"),
};
match mode.rerank {
None => tokens,
Some(cfg) if cfg.relevance_weight == 0.0 => format!("{tokens}_rerank_metadata"),
Some(cfg) => format!(
"{tokens}_rerank_blended_hl{:.0}d",
cfg.temporal_half_life_secs / 86_400.0
),
}
}
/// A retrieval configuration: how much of the score comes from each stage.
#[derive(Clone, Copy)]
struct Mode {
label: &'static str,
/// How the two retrieval stages are combined into one ranking.
fusion: Fusion,
/// How keyword tokens are normalised before indexing and querying.
tokens: TokenFilter,
/// Re-rank the retrieved candidates with recency and friends, relative to
/// the question's own date.
rerank: Option<ReRankConfig>,
}
impl Mode {
const fn weighted(label: &'static str, vector: f32, keyword: f32) -> Self {
Self {
label,
fusion: Fusion::Weighted { vector, keyword },
tokens: TokenFilter::Plain,
rerank: None,
}
}
#[cfg_attr(not(feature = "embeddings"), allow(dead_code))]
fn reranked(mut self, label: &'static str, rerank: ReRankConfig) -> Self {
self.label = label;
self.rerank = Some(rerank);
self
}
const fn stemmed(mut self, label: &'static str) -> Self {
self.label = label;
self.tokens = TokenFilter::Stemmed;
self
}
vector_weight: f32,
keyword_weight: f32,
}
/// The only mode available without real embeddings. Passing zero vectors with
/// `vector_weight = 0.0` is what made the vector stage inert.
const BM25_ONLY: Mode = Mode::weighted("BM25 only (vector stage inert)", 0.0, 1.0);
const BM25_ONLY: Mode = Mode {
label: "BM25 only (vector stage inert)",
vector_weight: 0.0,
keyword_weight: 1.0,
};
#[cfg(feature = "embeddings")]
const VECTOR_ONLY: Mode = Mode::weighted("Vector only (MiniLM + HNSW)", 1.0, 0.0);
const VECTOR_ONLY: Mode = Mode {
label: "Vector only (MiniLM + HNSW)",
vector_weight: 1.0,
keyword_weight: 0.0,
};
/// Tuned by `--sweep` over the full haystack. The former 0.7/0.3 was a
/// documented default that had never been searched, and the sweep found it
/// strictly dominated: 0.4/0.6 is better on Hit@1, Hit@5, Hit@10 and MRR at
/// both granularities.
#[cfg(feature = "embeddings")]
const HYBRID: Mode = Mode::weighted("Hybrid (0.4 vector / 0.6 BM25, tuned)", 0.4, 0.6);
/// Reciprocal rank fusion, the documented alternative to the weighted sum.
/// It ignores score magnitudes, so there is nothing to tune — which is the
/// claim being tested.
#[cfg(feature = "embeddings")]
const RRF: Mode = Mode {
label: "Hybrid (reciprocal rank fusion, k=60)",
fusion: Fusion::Rrf { k: 60.0 },
tokens: TokenFilter::Plain,
rerank: None,
const HYBRID: Mode = Mode {
label: "Hybrid (0.4 vector / 0.6 BM25, tuned)",
vector_weight: 0.4,
keyword_weight: 0.6,
};
/// The same two configurations with stemmed keyword tokens, so the tokenizer's
/// effect is isolated from everything else.
const BM25_STEMMED: Mode = BM25_ONLY.stemmed("BM25 only, stemmed tokens");
/// Re-ranking as it behaved before `relevance` was an input: the combined
/// score was recency + authority + activation only, so the retriever's own
/// ordering was discarded.
#[cfg(feature = "embeddings")]
fn hybrid_rerank_metadata_only() -> Mode {
HYBRID.reranked(
"Hybrid + rerank (metadata only, pre-fix)",
ReRankConfig {
relevance_weight: 0.0,
..ReRankConfig::default()
},
)
}
/// Re-ranking as it behaves now: relevance leads, recency nudges.
#[cfg(feature = "embeddings")]
fn hybrid_rerank_blended() -> Mode {
HYBRID.reranked(
"Hybrid + rerank (relevance + recency)",
ReRankConfig::default(),
)
}
/// The same blend at several half-lives. Decay is `2^(-age / half_life)`, so a
/// half-life far shorter than the gaps between memories sends every score to
/// zero and the signal vanishes; far longer and everything scores ~1 and it
/// vanishes the other way. The right value tracks how far apart the memories
/// actually are.
#[cfg(feature = "embeddings")]
fn hybrid_rerank_half_lives() -> Vec<Mode> {
[
("1 day", 86_400.0),
("7 days", 7.0 * 86_400.0),
("30 days", 30.0 * 86_400.0),
("90 days", 90.0 * 86_400.0),
]
.into_iter()
.map(|(label, half_life)| {
HYBRID.reranked(
Box::leak(format!("Hybrid + rerank, half-life {label}").into_boxed_str()),
ReRankConfig {
temporal_half_life_secs: half_life,
..ReRankConfig::default()
},
)
})
.collect()
}
#[cfg(feature = "embeddings")]
const HYBRID_STEMMED: Mode = HYBRID.stemmed("Hybrid 0.4/0.6, stemmed tokens");
/// Every 0.1 step of vector weight, keyword weight taking the remainder.
///
/// Labels are leaked to `&'static str` because `Mode::label` is a `&'static
@@ -215,11 +104,11 @@ fn sweep_modes() -> Vec<Mode> {
(0..=10)
.map(|i| {
let v = i as f32 / 10.0;
Mode::weighted(
Box::leak(format!("sweep v={v:.1} / k={:.1}", 1.0 - v).into_boxed_str()),
v,
1.0 - v,
)
Mode {
label: Box::leak(format!("sweep v={v:.1} / k={:.1}", 1.0 - v).into_boxed_str()),
vector_weight: v,
keyword_weight: 1.0 - v,
}
})
.collect()
}
@@ -292,37 +181,6 @@ struct Question {
haystack_session_ids: Vec<String>,
haystack_sessions: Vec<Vec<Turn>>,
answer_session_ids: Vec<String>,
/// One timestamp per haystack session, e.g. "2023/05/25 (Thu) 20:21".
#[serde(default)]
haystack_dates: Vec<String>,
}
/// Seconds since the epoch for a LongMemEval session date, which looks like
/// `2023/05/25 (Thu) 20:21`. Sessions are stored in chronological order, so a
/// date that cannot be parsed falls back to its position — order is preserved
/// even if the interval is not.
fn session_time(date: &str, position: usize) -> f64 {
let stamp = |y: i64, mo: i64, d: i64, h: i64, mi: i64| -> f64 {
// Days since 1970-01-01 via the civil-from-days algorithm.
let (y, mo) = if mo <= 2 { (y - 1, mo + 12) } else { (y, mo) };
let era = y.div_euclid(400);
let yoe = y - era * 400;
let doy = (153 * (mo - 3) + 2) / 5 + d - 1;
let doe = yoe * 365 + yoe / 4 - yoe / 100 + doy;
let days = era * 146_097 + doe - 719_468;
(days * 86_400 + h * 3_600 + mi * 60) as f64
};
let parse = || -> Option<f64> {
let (ymd, rest) = date.split_once(' ')?;
let mut ymd = ymd.split('/');
let y = ymd.next()?.parse().ok()?;
let mo = ymd.next()?.parse().ok()?;
let d = ymd.next()?.parse().ok()?;
let hm = rest.rsplit(' ').next()?;
let (h, mi) = hm.split_once(':')?;
Some(stamp(y, mo, d, h.parse().ok()?, mi.parse().ok()?))
};
parse().unwrap_or(1_000_000.0 + position as f64 * 86_400.0)
}
// ---------------------------------------------------------------------------
@@ -341,21 +199,11 @@ struct Metrics {
rr_turn: f64,
abstention_correct: u32,
abstention_total: u32,
/// Questions where the newest gold session outranked the older ones, out
/// of those with more than one gold session and at least one retrieved.
newest_gold_first: u32,
newest_gold_total: u32,
latency_ns: Vec<u64>,
count: u32,
}
impl Metrics {
/// `None` when no question in this bucket had multiple gold sessions.
fn newest_gold_first_pct(&self) -> Option<f64> {
(self.newest_gold_total > 0)
.then(|| self.newest_gold_first as f64 / self.newest_gold_total as f64 * 100.0)
}
fn hit1_session_pct(&self) -> f64 {
self.hit1_session as f64 / self.count.max(1) as f64 * 100.0
}
@@ -413,16 +261,6 @@ struct EvalResult {
hit5_turn: bool,
hit10_turn: bool,
rr_turn: Option<f64>,
/// For a question whose evidence spans several dated sessions (a
/// `knowledge-update`, where an earlier fact is superseded by a later
/// one): did the *newest* gold session outrank every older gold session
/// that was returned? `None` when the question has one gold session, or
/// when none were retrieved, so there is nothing to discriminate.
///
/// Plain recall cannot see this. LongMemEval labels *both* the stale and
/// the updated session as gold, so returning either counts as a hit — yet
/// only one of them answers the question correctly.
newest_gold_first: Option<bool>,
latency: Duration,
}
@@ -436,29 +274,21 @@ fn evaluate_question(
let mut config = MemoryConfig::new(dir.path().join("lme.h5"), "lme-bench", EMBEDDING_DIM);
config.wal_enabled = false;
config.compact_threshold = 0.0;
config.float16 = FLOAT16.load(std::sync::atomic::Ordering::Relaxed);
let mut memory = HDF5Memory::create(config).expect("failed to create HDF5Memory");
memory.set_token_filter(mode.tokens);
// Build MemoryEntry list from all haystack sessions
let mut entries: Vec<MemoryEntry> = Vec::new();
let mut turn_has_answer: Vec<bool> = Vec::new();
let mut ts = 1_000_000.0f64;
for (sess_idx, session) in q.haystack_sessions.iter().enumerate() {
let sess_id = q
.haystack_session_ids
.get(sess_idx)
.map(String::as_str)
.unwrap_or("unknown");
// Real session dates, not a synthetic counter: anything that decays
// with age needs true intervals, not just the right order.
let session_start = q
.haystack_dates
.get(sess_idx)
.map_or(sess_idx as f64 * 86_400.0, |d| session_time(d, sess_idx));
for (turn_idx, turn) in session.iter().enumerate() {
// Spread a session's turns over the minutes following its start.
let ts = session_start + turn_idx as f64 * 60.0;
for turn in session {
entries.push(MemoryEntry {
chunk: turn.content.clone(),
embedding: embedding_for(embeddings, &turn.content),
@@ -472,6 +302,7 @@ fn evaluate_question(
},
});
turn_has_answer.push(turn.has_answer);
ts += 1.0;
}
}
@@ -488,87 +319,17 @@ fn evaluate_question(
// Set of session IDs that contain the answer
let answer_sess_set: HashSet<&str> = q.answer_session_ids.iter().map(String::as_str).collect();
// When each gold session was recorded, so "newest" is by date rather than
// by position (the two agree in this dataset, but the metric should not
// depend on that).
let gold_times: HashMap<&str, f64> = q
.haystack_session_ids
.iter()
.enumerate()
.filter(|(_, sid)| answer_sess_set.contains(sid.as_str()))
.map(|(i, sid)| {
let t = q
.haystack_dates
.get(i)
.map_or(i as f64 * 86_400.0, |d| session_time(d, i));
(sid.as_str(), t)
})
.collect();
let query_emb = embedding_for(embeddings, &q.question);
let t0 = Instant::now();
// Re-ranking only reorders; it needs a candidate pool larger than `top_k`
// to have anything to promote.
let pool = if mode.rerank.is_some() {
top_k * 4
} else {
top_k
};
let mut results = memory.hybrid_search_with(&query_emb, &q.question, mode.fusion, pool);
if let Some(config) = mode.rerank {
// "Now" is the moment the question was asked, so decay measures how
// stale each memory was at that point.
let now = session_time(&q.question_date, q.haystack_sessions.len());
let inputs: Vec<RerankInput> = results
.iter()
.map(|r| RerankInput {
index: r.index,
timestamp: r.timestamp,
source_channel: r.source_channel.clone(),
raw_activation: r.activation,
relevance: r.score,
})
.collect();
let order: Vec<usize> = rerank(&inputs, &config, now)
.into_iter()
.map(|r| r.index)
.collect();
let by_index: HashMap<usize, SearchResult> =
results.into_iter().map(|r| (r.index, r)).collect();
results = order
.into_iter()
.filter_map(|i| by_index.get(&i).cloned())
.collect();
}
results.truncate(top_k);
let results = memory.hybrid_search(
&query_emb,
&q.question,
mode.vector_weight,
mode.keyword_weight,
top_k,
);
let latency = t0.elapsed();
// Rank of the best-placed result from each gold session.
let mut first_rank: HashMap<&str, usize> = HashMap::new();
for (rank, result) in results.iter().enumerate() {
let sid = memory.cache.session_ids[result.index].as_str();
if let Some((gold_sid, _)) = gold_times.get_key_value(sid) {
first_rank.entry(gold_sid).or_insert(rank);
}
}
let newest_gold_first = if gold_times.len() < 2 || first_rank.is_empty() {
None
} else {
// The newest gold session must be retrieved, and no older gold session
// may outrank it.
let newest = gold_times
.iter()
.max_by(|a, b| a.1.total_cmp(b.1))
.map(|(sid, _)| *sid)
.expect("at least two gold sessions");
Some(match first_rank.get(newest) {
Some(&newest_rank) => first_rank
.iter()
.all(|(sid, &rank)| *sid == newest || rank > newest_rank),
None => false,
})
};
// Session-level recall
let mut hit1_session = false;
let mut hit5_session = false;
@@ -623,7 +384,6 @@ fn evaluate_question(
hit5_turn,
hit10_turn,
rr_turn,
newest_gold_first,
latency,
}
}
@@ -712,7 +472,10 @@ fn print_report(
println!(" LongMemEval Benchmark — {}", mode.label);
println!("=================================================================");
println!();
println!("Mode: {}", describe(mode));
println!(
"Mode: vector_weight={:.1} / keyword_weight={:.1}",
mode.vector_weight, mode.keyword_weight
);
println!();
println!("Scoring target: RETRIEVAL RECALL (did the gold memory land in top-k).");
println!(" No answer is generated or scored. This is NOT the official");
@@ -775,24 +538,6 @@ fn print_report(
);
println!();
if let Some(pct) = overall.newest_gold_first_pct() {
println!(
"## Recency Discrimination (n={})",
overall.newest_gold_total
);
println!(
" Newest gold session ranked first: {}/{} ({pct:.1}%)",
overall.newest_gold_first, overall.newest_gold_total
);
println!(
" Questions whose evidence spans several dated sessions — a fact and\n \
its later correction. Both sessions are labelled gold, so recall\n \
scores either as a hit; this asks whether the *current* one came\n \
first. A retriever with no sense of time scores near chance."
);
println!();
}
if overall.abstention_total > 0 {
println!("## Abstention Accuracy");
println!(
@@ -857,7 +602,10 @@ fn print_report(
println!("```json");
println!("{{");
println!(" \"benchmark\": \"longmemeval\",");
println!(" \"mode\": \"{}\",", describe(mode));
println!(
" \"mode\": \"vector_{:.1}_keyword_{:.1}\",",
mode.vector_weight, mode.keyword_weight
);
println!(" \"dataset_variant\": \"{}\",", profile.variant());
println!(" \"scoring_target\": \"retrieval_recall\",");
println!(" \"k\": 10,");
@@ -906,14 +654,6 @@ fn print_report(
} else {
println!(" \"abstention_accuracy\": null,");
}
match overall.newest_gold_first_pct() {
Some(pct) => println!(
" \"newest_gold_first\": {:.4}, \"newest_gold_n\": {},",
pct / 100.0,
overall.newest_gold_total
),
None => println!(" \"newest_gold_first\": null,"),
}
println!(" \"latency_us\": {{");
println!(
" \"avg\": {:.1}, \"p50\": {:.1}, \"p95\": {:.1}, \"p99\": {:.1}",
@@ -936,8 +676,6 @@ fn main() {
let mut limit: Option<usize> = None;
let mut weights_dir: Option<String> = None;
let mut sweep = false;
#[cfg_attr(not(feature = "embeddings"), allow(unused_mut, unused_variables))]
let mut rerank_sweep = false;
let mut args = std::env::args().skip(1);
while let Some(arg) = args.next() {
match arg.as_str() {
@@ -946,20 +684,6 @@ fn main() {
limit = Some(v.parse().expect("--limit must be a positive integer"));
}
"--sweep" => sweep = true,
"--float16" => {
FLOAT16.store(true, std::sync::atomic::Ordering::Relaxed);
eprintln!("Stores use MemoryConfig::float16 (half-precision embeddings)");
}
"--rerank-sweep" => {
// Re-ranking needs the vector stage to have candidates worth
// reordering, so this is an embeddings-only comparison.
#[cfg(feature = "embeddings")]
{
rerank_sweep = true;
}
#[cfg(not(feature = "embeddings"))]
eprintln!("warning: --rerank-sweep needs --features embeddings; ignoring");
}
"--embeddings" => {
weights_dir = Some(args.next().expect("--embeddings needs a directory"));
}
@@ -978,12 +702,6 @@ fn main() {
BM25-only, vector-only, and hybrid separately. Requires\n\
--features embeddings; without it the vector stage is\n\
inert and only the BM25 row is produced.\n\
--rerank-sweep\n\
compare re-ranking off, metadata-only (the old\n\
behaviour) and blended at several half-lives.\n\
--float16\n\
build each store with MemoryConfig::float16, to\n\
compare retrieval on half-precision embeddings.\n\
--sweep instead of the three named modes, sweep vector_weight\n\
from 0.0 to 1.0 in 0.1 steps. The 0.7/0.3 default was\n\
never searched; this is what searches it."
@@ -1049,34 +767,19 @@ fn main() {
{
if sweep {
sweep_modes()
} else if rerank_sweep {
let mut modes = vec![HYBRID, hybrid_rerank_metadata_only()];
modes.extend(hybrid_rerank_half_lives());
modes
} else {
vec![
BM25_ONLY,
VECTOR_ONLY,
HYBRID,
RRF,
BM25_STEMMED,
HYBRID_STEMMED,
hybrid_rerank_metadata_only(),
hybrid_rerank_blended(),
]
vec![BM25_ONLY, VECTOR_ONLY, HYBRID]
}
}
#[cfg(not(feature = "embeddings"))]
{
vec![BM25_ONLY, BM25_STEMMED]
vec![BM25_ONLY]
}
} else {
if sweep {
eprintln!("warning: --sweep needs --embeddings; running BM25 only");
}
// Stemming is a property of the keyword stage, so it can be compared
// without a model.
vec![BM25_ONLY, BM25_STEMMED]
vec![BM25_ONLY]
};
for (mode_idx, mode) in modes.iter().enumerate() {
@@ -1179,14 +882,6 @@ fn run_mode(
entry.rr_turn += rr;
overall.rr_turn += rr;
}
if let Some(newest_first) = result.newest_gold_first {
entry.newest_gold_total += 1;
overall.newest_gold_total += 1;
if newest_first {
entry.newest_gold_first += 1;
overall.newest_gold_first += 1;
}
}
let ns = result.latency.as_nanos() as u64;
entry.latency_ns.push(ns);
@@ -1198,30 +893,3 @@ fn run_mode(
eprintln!();
print_report(&overall, &by_type, profile, mode);
}
#[cfg(test)]
mod tests {
use super::session_time;
#[test]
fn session_dates_parse_to_the_right_instant() {
// Reference values from Python's datetime, UTC.
for (date, expected) in [
("2023/05/25 (Thu) 20:21", 1_685_046_060.0),
("1970/01/01 (Thu) 00:00", 0.0),
("2000/02/29 (Tue) 12:00", 951_825_600.0),
("2023/12/31 (Sun) 23:59", 1_704_067_140.0),
("2024/03/01 (Fri) 00:00", 1_709_251_200.0),
] {
assert_eq!(session_time(date, 0), expected, "{date}");
}
}
#[test]
fn unparseable_dates_fall_back_to_position_order() {
let a = session_time("not a date", 0);
let b = session_time("", 1);
let c = session_time("2023/13/99 (???) 99:99", 2);
assert!(a < b && b < c, "fallback must preserve session order");
}
}
crates/clawhdf5-bench/src/bin/read_harness.rs
-176
View File
@@ -1,176 +0,0 @@
//! HDF5 read-path measurement harness: full reads vs. hyperslab selections on
//! a chunked 2-D dataset, compressed and uncompressed, plus a contiguous one.
//!
//! The question it answers for every read-path change: does the cost of a
//! selection scale with the *selection*, or with the whole dataset?
//!
//! ```text
//! cargo run --release -p clawhdf5-bench --bin read_harness
//! cargo run --release -p clawhdf5-bench --bin read_harness -- --large # 512 MB
//! ```
use std::time::{Duration, Instant};
use clawhdf5::{File, FileBuilder};
use clawhdf5_format::selection::Selection;
const CHUNK: u64 = 256;
struct Layout {
name: &'static str,
chunked: bool,
deflate: bool,
}
const LAYOUTS: [Layout; 3] = [
Layout {
name: "chunked + deflate",
chunked: true,
deflate: true,
},
Layout {
name: "chunked",
chunked: true,
deflate: false,
},
Layout {
name: "contiguous",
chunked: false,
deflate: false,
},
];
/// Smooth-ish, compressible data whose value encodes its position, so a read
/// can be verified exactly.
fn value(row: u64, col: u64) -> f64 {
(row * 100_003 + col) as f64 * 0.5
}
fn write_file(path: &std::path::Path, rows: u64, cols: u64) {
let data: Vec<f64> = (0..rows)
.flat_map(|r| (0..cols).map(move |c| value(r, c)))
.collect();
let mut builder = FileBuilder::new();
for (i, layout) in LAYOUTS.iter().enumerate() {
let ds = builder.create_dataset(&format!("d{i}"));
ds.with_f64_data(&data).with_shape(&[rows, cols]);
if layout.chunked {
ds.with_chunks(&[CHUNK, CHUNK]);
}
if layout.deflate {
ds.with_deflate(4);
}
}
builder.write(path).unwrap();
}
fn median(mut samples: Vec<Duration>) -> Duration {
samples.sort();
samples[samples.len() / 2]
}
fn time<T>(reps: usize, mut f: impl FnMut() -> T) -> Duration {
median(
(0..reps)
.map(|_| {
let t = Instant::now();
std::hint::black_box(f());
t.elapsed()
})
.collect(),
)
}
fn slab(start: [u64; 2], count: [u64; 2]) -> Selection {
Selection::Hyperslab {
start: start.to_vec(),
stride: vec![1, 1],
count: count.to_vec(),
block: vec![1, 1],
}
}
fn main() {
let large = std::env::args().any(|a| a == "--large");
let (rows, cols) = if large { (8192, 8192) } else { (4096, 2048) };
let total_mb = (rows * cols * 8) as f64 / (1 << 20) as f64;
if cfg!(debug_assertions) {
eprintln!("warning: debug build — numbers are meaningless. Use --release.");
}
let dir = tempfile::TempDir::new().unwrap();
let path = dir.path().join("read_harness.h5");
write_file(&path, rows, cols);
let file_mb = std::fs::metadata(&path).unwrap().len() as f64 / (1 << 20) as f64;
println!("## Read harness");
println!(
"\n{rows} x {cols} f64 ({total_mb:.0} MB per dataset), chunks {CHUNK} x {CHUNK}, file {file_mb:.0} MB\n"
);
// (label, selection, elements selected)
let selections: Vec<(&str, Selection, u64)> = vec![
(
"64 x 64 window (1 chunk)",
slab([300, 300], [64, 64]),
64 * 64,
),
(
"512 x 512 window (4-9 chunks)",
slab([1000, 700], [512, 512]),
512 * 512,
),
("one row", slab([rows / 2, 0], [1, cols]), cols),
("one column", slab([0, cols / 2], [rows, 1]), rows),
];
println!("| layout | read | selected | time ms | MB/s of selection | vs full read |");
println!("|---|---|---:|---:|---:|---:|");
for (i, layout) in LAYOUTS.iter().enumerate() {
// Fresh handle per layout so one dataset's cached chunks don't help
// (or evict) another's.
let file = File::open(&path).unwrap();
let ds = file.dataset(&format!("d{i}")).unwrap();
let full_cold = time(1, || ds.read_f64().unwrap());
let full = time(3, || ds.read_f64().unwrap());
println!(
"| {} | full (first) | {total_mb:.0} MB | {:.1} | {:.0} | |",
layout.name,
full_cold.as_secs_f64() * 1e3,
total_mb / full_cold.as_secs_f64()
);
println!(
"| {} | full (repeat) | {total_mb:.0} MB | {:.1} | {:.0} | 1.00x |",
layout.name,
full.as_secs_f64() * 1e3,
total_mb / full.as_secs_f64()
);
for (label, selection, elements) in &selections {
// A fresh handle again: measure the selection on its own, not
// served from chunks the full read just cached.
let file = File::open(&path).unwrap();
let ds = file.dataset(&format!("d{i}")).unwrap();
let got = ds.read_f64_selection(selection).unwrap();
assert_eq!(got.len() as u64, *elements, "{label}");
if let Selection::Hyperslab { start, .. } = selection {
assert_eq!(got[0], value(start[0], start[1]), "{label}: wrong data");
}
let took = time(5, || {
let file = File::open(&path).unwrap();
let ds = file.dataset(&format!("d{i}")).unwrap();
ds.read_f64_selection(selection).unwrap()
});
let mb = (*elements * 8) as f64 / (1 << 20) as f64;
println!(
"| {} | {label} | {:.2} MB | {:.2} | {:.0} | {:.3}x |",
layout.name,
mb,
took.as_secs_f64() * 1e3,
mb / took.as_secs_f64(),
took.as_secs_f64() / full_cold.as_secs_f64()
);
}
}
}
crates/clawhdf5-bench/src/bin/search_harness.rs
-1150
View File
File diff suppressed because it is too large Load Diff
crates/clawhdf5-cli/Cargo.toml
+3 -4
View File
@@ -1,11 +1,10 @@
[package]
name = "clawhdf5-cli"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
license = "MIT"
description = "CLI for clawhdf5 agent memory — create, save, search, recall, stats"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
keywords = ["hdf5", "ai", "memory", "agent", "cli"]
categories = ["command-line-utilities", "science"]
readme = "../../README.md"
@@ -15,7 +14,7 @@ name = "clawhdf5"
path = "src/main.rs"
[dependencies]
clawhdf5-agent = { path = "../clawhdf5-agent", version = "2.7.0" }
clawhdf5-agent = { path = "../clawhdf5-agent", version = "2.1.0" }
clap = { version = "4", features = ["derive", "env"] }
serde_json = "1"
serde = { workspace = true }
crates/clawhdf5-cli/src/main.rs
+17 -165
View File
@@ -1,22 +1,15 @@
use std::path::{Path, PathBuf};
use std::path::PathBuf;
use clap::{Parser, Subcommand};
use clawhdf5_agent::signing::{self, SigningKey, VerifyingKey};
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry};
/// ClawhDF5 — HDF5-backed cognitive memory for AI agents
#[derive(Parser)]
#[command(name = "clawhdf5", version, about)]
struct Cli {
/// Path to the .h5 memory file (not needed for `keygen`)
/// Path to the .h5 memory file
#[arg(short, long, env = "CLAWHDF5_PATH")]
path: Option<PathBuf>,
/// File holding an Ed25519 signing key (64 hex characters, from
/// `keygen`). Every checkpoint this command makes is then signed; a
/// signed store refuses to checkpoint without it.
#[arg(long, env = "CLAWHDF5_SIGNING_KEY", global = true)]
signing_key: Option<PathBuf>,
path: PathBuf,
#[command(subcommand)]
command: Commands,
@@ -35,22 +28,6 @@ enum Commands {
/// Enable write-ahead log
#[arg(long)]
wal: bool,
/// Hold the vector index's copy of the embeddings as f32 instead of
/// the default int8 (which uses a quarter of the memory and is faster
/// at equal recall)
#[arg(long)]
f32_index: bool,
/// Accepted for compatibility; int8 is now the default
#[arg(long, hide = true, conflicts_with = "f32_index")]
quantized_index: bool,
/// Store embeddings as full-precision f32 instead of the default
/// half precision (float16: half the bytes, about three significant
/// digits, values within ±65504)
#[arg(long)]
f32: bool,
/// Accepted for compatibility; float16 is now the default
#[arg(long, hide = true, conflicts_with = "f32")]
float16: bool,
},
/// Save a memory entry (reads JSON from stdin or --json)
Save {
@@ -98,38 +75,6 @@ enum Commands {
/// Destination path
dest: PathBuf,
},
/// Generate an Ed25519 signing key for signed checkpoints
Keygen {
/// Where to write the secret key (created new, owner-only on Unix)
#[arg(long)]
out: PathBuf,
},
/// Verify a signed store against a public key; exit status 2 if not valid
Verify {
/// The trusted public key: 64 hex characters, or a file holding them
#[arg(long)]
public_key: String,
},
}
fn read_signing_key(path: &Path) -> Result<SigningKey, Box<dyn std::error::Error>> {
let text = std::fs::read_to_string(path)
.map_err(|e| format!("cannot read signing key {}: {e}", path.display()))?;
let bytes = signing::from_hex::<32>(&text)
.ok_or_else(|| format!("{} is not a 64-hex-character key", path.display()))?;
Ok(SigningKey::from_bytes(&bytes))
}
/// Open for writing, with the signing key applied if one was given.
fn open_writable(
path: &Path,
key: &Option<SigningKey>,
) -> Result<HDF5Memory, Box<dyn std::error::Error>> {
let mut mem = HDF5Memory::open(path)?;
if let Some(k) = key {
mem.set_signing_key(k.clone());
}
Ok(mem)
}
fn main() {
@@ -142,76 +87,17 @@ fn main() {
}
fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
if let Commands::Keygen { out } = &cli.command {
let key = signing::generate_key();
let mut opts = std::fs::OpenOptions::new();
opts.write(true).create_new(true);
#[cfg(unix)]
{
use std::os::unix::fs::OpenOptionsExt;
opts.mode(0o600);
}
use std::io::Write;
let mut f = opts
.open(out)
.map_err(|e| format!("cannot create {}: {e}", out.display()))?;
writeln!(f, "{}", signing::to_hex(&key.to_bytes()))?;
let j = serde_json::json!({
"status": "generated",
"secret_key_file": out.display().to_string(),
"public_key": signing::to_hex(&key.verifying_key().to_bytes()),
});
println!("{}", serde_json::to_string_pretty(&j)?);
return Ok(());
}
let path = cli
.path
.clone()
.ok_or("--path (or CLAWHDF5_PATH) is required")?;
let key = cli
.signing_key
.as_deref()
.map(read_signing_key)
.transpose()?;
match cli.command {
Commands::Create {
agent_id,
dim,
wal,
f32_index,
quantized_index: _,
f32,
float16: _,
} => {
let mut config = MemoryConfig::new(path.clone(), &agent_id, dim);
Commands::Create { agent_id, dim, wal } => {
let mut config = MemoryConfig::new(cli.path.clone(), &agent_id, dim);
config.wal_enabled = wal;
// As with --f32-index: only ever switch the library default off.
if f32 {
config.float16 = false;
}
let config_float16 = config.float16;
// Only ever switch *off* the library default: assigning the flag
// outright would force every CLI-created store back to f32 unless
// the caller knew to ask for int8.
if f32_index {
config.quantized_index = false;
}
let config_quantized = config.quantized_index;
let mut mem = HDF5Memory::create(config)?;
// Sign straight away, so the store is never on disk unsigned.
if let Some(k) = &key {
mem.set_signing_key(k.clone());
mem.flush_wal()?;
}
let mem = HDF5Memory::create(config)?;
let j = serde_json::json!({
"status": "created",
"path": path.display().to_string(),
"path": cli.path.display().to_string(),
"agent_id": agent_id,
"embedding_dim": dim,
"wal_enabled": wal,
"quantized_index": config_quantized,
"float16": config_float16,
"signed": mem.is_signed(),
"count": mem.count(),
});
println!("{}", serde_json::to_string_pretty(&j)?);
@@ -228,7 +114,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
};
let entry: MemoryEntry = serde_json::from_str(&input)?;
let mut mem = open_writable(&path, &key)?;
let mut mem = HDF5Memory::open(&cli.path)?;
let idx = mem.save(entry)?;
let j = serde_json::json!({ "status": "saved", "index": idx, "count": mem.count() });
println!("{}", serde_json::to_string(&j)?);
@@ -242,7 +128,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
keyword_weight,
} => {
let emb: Vec<f32> = serde_json::from_str(&embedding)?;
let mut mem = open_writable(&path, &key)?;
let mut mem = HDF5Memory::open(&cli.path)?;
let results = mem.hybrid_search(&emb, &query, vector_weight, keyword_weight, top_k);
let j: Vec<serde_json::Value> = results
.iter()
@@ -260,7 +146,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::Recall { index } => {
let mem = HDF5Memory::open_read_only(&path)?;
let mem = HDF5Memory::open(&cli.path)?;
match mem.get_chunk(index) {
Some(content) => {
let j = serde_json::json!({ "index": index, "chunk": content });
@@ -274,23 +160,22 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::Stats => {
let mem = HDF5Memory::open_read_only(&path)?;
let mem = HDF5Memory::open(&cli.path)?;
let cfg = mem.config();
let j = serde_json::json!({
"path": path.display().to_string(),
"path": cli.path.display().to_string(),
"agent_id": cfg.agent_id,
"embedding_dim": cfg.embedding_dim,
"count": mem.count(),
"active": mem.count_active(),
"wal_enabled": cfg.wal_enabled,
"wal_pending": mem.wal_pending_count(),
"signed": mem.is_signed(),
});
println!("{}", serde_json::to_string_pretty(&j)?);
}
Commands::FlushWal => {
let mut mem = open_writable(&path, &key)?;
let mut mem = HDF5Memory::open(&cli.path)?;
let before = mem.wal_pending_count();
mem.flush_wal()?;
let j = serde_json::json!({
@@ -302,7 +187,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::AgentsMd { output } => {
let mem = HDF5Memory::open_read_only(&path)?;
let mem = HDF5Memory::open(&cli.path)?;
let md = mem.generate_agents_md();
match output {
Some(p) => {
@@ -314,7 +199,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::Export => {
let mem = HDF5Memory::open_read_only(&path)?;
let mem = HDF5Memory::open(&cli.path)?;
for i in 0..mem.count() {
if let Some(chunk) = mem.get_chunk(i) {
let j = serde_json::json!({ "index": i, "chunk": chunk });
@@ -323,44 +208,11 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
}
Commands::Keygen { .. } => unreachable!("handled before opening a store"),
Commands::Verify { public_key } => {
let text = if Path::new(&public_key).is_file() {
std::fs::read_to_string(&public_key)?
} else {
public_key
};
let bytes = signing::from_hex::<32>(&text)
.ok_or("--public-key must be 64 hex characters or a file holding them")?;
let trusted = VerifyingKey::from_bytes(&bytes)?;
let r = HDF5Memory::verify(&path, &trusted)?;
let j = serde_json::json!({
"valid": r.is_valid(),
"signed": r.signed,
"key_matches": r.key_matches,
"signature_valid": r.signature_valid,
"records_match": r.records_match,
"settings_match": r.settings_match,
"sessions_match": r.sessions_match,
"graph_match": r.graph_match,
"changed_records": r.changed_records,
"record_count": r.record_count,
"signed_record_count": r.signed_record_count,
"signed_by": r.public_key.map(|k| signing::to_hex(&k)),
"wal_entries_unsigned": r.wal_entries_unsigned,
});
println!("{}", serde_json::to_string_pretty(&j)?);
if !r.is_valid() {
std::process::exit(2);
}
}
Commands::Snapshot { dest } => {
let _result = clawhdf5_agent::storage::snapshot_file(&path, &dest)?;
let _result = clawhdf5_agent::storage::snapshot_file(&cli.path, &dest)?;
let j = serde_json::json!({
"status": "snapshot_created",
"source": path.display().to_string(),
"source": cli.path.display().to_string(),
"dest": dest.display().to_string(),
});
println!("{}", serde_json::to_string(&j)?);
crates/clawhdf5-derive/Cargo.toml
+2 -3
View File
@@ -1,11 +1,10 @@
[package]
name = "clawhdf5-derive"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "Derive macros for rustyhdf5 HDF5 traits"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
readme = "README.md"
keywords = ["hdf5", "derive", "macros", "science"]
categories = ["development-tools::procedural-macro-helpers"]
crates/clawhdf5-filters/Cargo.toml
+4 -9
View File
@@ -1,11 +1,10 @@
[package]
name = "clawhdf5-filters"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "Filter and compression pipeline for clawhdf5"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
readme = "README.md"
keywords = ["hdf5", "compression", "deflate", "filters"]
categories = ["compression", "science"]
@@ -26,12 +25,8 @@ name = "compression_bench"
harness = false
[features]
# Pure-Rust zlib-rs by default; `fast-deflate` (zlib-ng, C) overrides it.
default = ["zlib-rs"]
default = ["fast-deflate"]
fast-deflate = ["flate2/zlib-ng"]
system-zlib = ["flate2/zlib-default"]
# `runtime_detection` gives zlib-rs `std`, which it needs to detect and use
# SIMD at runtime. flate2 enables it by default, but we build flate2 with
# default-features = false, and without it zlib-rs inflates 3.5x slower.
zlib-rs = ["flate2/zlib-rs", "flate2/runtime_detection"]
zlib-rs = ["flate2/zlib-rs"]
apple-compression = []
crates/clawhdf5-filters/README.md
+4 -6
View File
@@ -8,18 +8,16 @@ Filter and compression pipeline for clawhdf5.
## Features
- DEFLATE compression/decompression
- Pure-Rust deflate via zlib-rs (default, `zlib-rs` feature)
- zlib-ng instead, if you want it (`fast-deflate` feature; C, needs cmake)
- Fast deflate via zlib-ng (`fast-deflate` feature)
- Apple Compression framework support (`apple-compression` feature)
## Usage
```rust
use clawhdf5_filters::{deflate_compress, deflate_decompress};
use clawhdf5_filters::{deflate_decode, deflate_encode};
let compressed = deflate_compress(&data, 6).unwrap();
// The second argument bounds the output: the expected decompressed size.
let decompressed = deflate_decompress(&compressed, data.len()).unwrap();
let compressed = deflate_encode(&data, 6).unwrap();
let decompressed = deflate_decode(&compressed).unwrap();
```
## License
crates/clawhdf5-filters/src/fast_deflate.rs
+48 -111
View File
@@ -1,13 +1,12 @@
//! Deflate backends: Apple Compression Framework, zlib-ng and zlib-rs.
//! Fast deflate backends: Apple Compression Framework and zlib-ng.
//!
//! Backend selection priority (decompression & compression):
//! 1. Apple Compression Framework (macOS only, `apple-compression` feature)
//! 2. flate2 with zlib-ng (`fast-deflate`), else zlib-rs (`zlib-rs`, the
//! default), else miniz_oxide
//! 2. flate2 with zlib-ng backend (`fast-deflate` feature) or miniz_oxide (default)
//!
//! The Apple Compression Framework uses hardware-accelerated zlib on Apple Silicon
//! and is typically the fastest option on macOS. zlib-rs is a pure-Rust port of
//! zlib-ng; see `BENCHMARKS.md` for how the two compare.
//! and is typically the fastest option on macOS. zlib-ng is the fastest portable
//! option and what C HDF5 uses internally.
// ---------------------------------------------------------------------------
// Apple Compression Framework FFI (macOS only)
@@ -244,117 +243,65 @@ mod apple {
}
// ---------------------------------------------------------------------------
// One-shot (de)compression via flate2 (whichever backend flate2 was built with)
//
// The whole input goes to the codec in one call, into an output buffer sized
// up front. `flate2::read::ZlibDecoder` / `write::ZlibEncoder` stream through a
// 32 KiB buffer instead, which cost zlib-rs up to 3.7x against zlib-ng on a
// 1 MB chunk. clawhdf5-format's deflate filter does the same; see
// `BENCHMARKS.md`, "Deflate backend".
// Streaming decompression via flate2 (uses zlib-ng when fast-deflate enabled)
// ---------------------------------------------------------------------------
/// Decompress into a buffer pre-sized to `output_size`, the expected
/// decompressed length (known for HDF5 chunks). Output longer than that is an
/// error, as is a stream that ends early.
/// Streaming decompress with pre-allocated output buffer.
///
/// When the output size is known (typical for HDF5 chunks), this avoids
/// dynamic reallocation by writing directly into a pre-sized buffer.
pub(crate) fn flate2_decompress_preallocated(
data: &[u8],
output_size: usize,
) -> Result<Vec<u8>, String> {
inflate_bounded(data, output_size, output_size)
use std::io::Read;
let mut decoder = flate2::read::ZlibDecoder::new(data);
let mut output = vec![0u8; output_size];
let mut total_read = 0;
loop {
match decoder.read(&mut output[total_read..]) {
Ok(0) => break,
Ok(n) => total_read += n,
Err(e) => return Err(e.to_string()),
}
}
output.truncate(total_read);
Ok(output)
}
/// Absolute ceiling on decompressed output when the caller has no size hint,
/// preventing unbounded allocation from a hostile/corrupted zlib stream.
const MAX_DECOMPRESS_SIZE: usize = 256 * 1024 * 1024;
/// Decompress with no size hint, bounded by [`MAX_DECOMPRESS_SIZE`] so a
/// hostile zlib stream cannot force arbitrarily large allocation (a "zlib
/// bomb").
/// Streaming decompress with dynamic sizing (when output size is unknown).
///
/// Bounded by [`MAX_DECOMPRESS_SIZE`] since there is no chunk-size hint to
/// validate against here — an unbounded `read_to_end` would let a hostile
/// zlib stream force arbitrarily large allocation (a "zlib bomb").
pub(crate) fn flate2_decompress_streaming(data: &[u8]) -> Result<Vec<u8>, String> {
let hint = data.len().saturating_mul(4).min(1 << 20);
inflate_bounded(data, hint, MAX_DECOMPRESS_SIZE).map_err(|e| {
if e.ends_with("exceeds size limit") {
format!(
use std::io::Read;
let decoder = flate2::read::ZlibDecoder::new(data);
let mut result = Vec::new();
decoder
.take(MAX_DECOMPRESS_SIZE as u64 + 1)
.read_to_end(&mut result)
.map_err(|e| e.to_string())?;
if result.len() > MAX_DECOMPRESS_SIZE {
return Err(format!(
"decompressed output exceeds {} MiB limit",
MAX_DECOMPRESS_SIZE / 1024 / 1024
)
} else {
e
));
}
})
Ok(result)
}
/// Inflate a zlib stream, starting from `size_hint` bytes of output and
/// failing past `limit`.
fn inflate_bounded(data: &[u8], size_hint: usize, limit: usize) -> Result<Vec<u8>, String> {
use flate2::{Decompress, FlushDecompress, Status};
// One byte of headroom past the limit distinguishes an over-size stream
// from one that legitimately ends exactly at the limit.
let max_capacity = limit.saturating_add(1);
let mut out = Vec::new();
out.try_reserve_exact(size_hint.clamp(1, max_capacity))
.map_err(|e| format!("deflate: cannot allocate output: {e}"))?;
let mut inflater = Decompress::new(true);
loop {
let (in_before, out_before) = (inflater.total_in(), inflater.total_out());
let status = inflater
.decompress_vec(
&data[in_before as usize..],
&mut out,
FlushDecompress::Finish,
)
.map_err(|e| format!("deflate: {e}"))?;
if out.len() > limit {
return Err("deflate: output exceeds size limit".into());
}
match status {
Status::StreamEnd => return Ok(out),
Status::Ok | Status::BufError if out.len() == out.capacity() => {
let grow = out.capacity().min(max_capacity - out.capacity()).max(1);
out.try_reserve_exact(grow)
.map_err(|e| format!("deflate: cannot allocate output: {e}"))?;
}
Status::Ok | Status::BufError => {
if inflater.total_in() as usize >= data.len()
|| (inflater.total_in(), inflater.total_out()) == (in_before, out_before)
{
return Err("deflate: truncated stream".into());
}
}
}
}
}
/// Compress data using flate2 (zlib-ng, zlib-rs or miniz_oxide; see module docs).
/// Compress data using flate2 (zlib-ng when fast-deflate enabled, else miniz_oxide).
pub(crate) fn flate2_compress(data: &[u8], level: u32) -> Result<Vec<u8>, String> {
use flate2::{Compress, Compression, FlushCompress, Status};
// zlib's compressBound, plus the zlib header and trailer.
let bound = data.len() + (data.len() >> 12) + (data.len() >> 14) + (data.len() >> 25) + 13 + 6;
let mut out = Vec::new();
out.try_reserve_exact(bound)
.map_err(|e| format!("deflate: cannot allocate output: {e}"))?;
let mut deflater = Compress::new(Compression::new(level), true);
loop {
let (in_before, out_before) = (deflater.total_in(), deflater.total_out());
let status = deflater
.compress_vec(&data[in_before as usize..], &mut out, FlushCompress::Finish)
.map_err(|e| format!("deflate: {e}"))?;
match status {
Status::StreamEnd => return Ok(out),
Status::Ok | Status::BufError if out.len() == out.capacity() => out
.try_reserve(out.capacity().max(4096))
.map_err(|e| format!("deflate: cannot allocate output: {e}"))?,
Status::Ok | Status::BufError => {
if (deflater.total_in(), deflater.total_out()) == (in_before, out_before) {
return Err("deflate: encoder made no progress".into());
}
}
}
}
use std::io::Write;
let mut encoder = flate2::write::ZlibEncoder::new(Vec::new(), flate2::Compression::new(level));
encoder.write_all(data).map_err(|e| e.to_string())?;
encoder.finish().map_err(|e| e.to_string())
}
// ---------------------------------------------------------------------------
@@ -365,7 +312,7 @@ pub(crate) fn flate2_compress(data: &[u8], level: u32) -> Result<Vec<u8>, String
///
/// Selection order:
/// 1. Apple Compression Framework (macOS + `apple-compression` feature)
/// 2. flate2 (zlib-ng with `fast-deflate`, else zlib-rs, else miniz_oxide)
/// 2. flate2 (zlib-ng with `fast-deflate`, otherwise miniz_oxide)
///
/// When `output_hint` > 0, pre-allocates the output buffer for zero-copy
/// decompression (avoids reallocation).
@@ -397,7 +344,7 @@ pub fn decompress(data: &[u8], output_hint: usize) -> Result<Vec<u8>, String> {
///
/// Selection order:
/// 1. Apple Compression Framework (macOS + `apple-compression` feature)
/// 2. flate2 (zlib-ng with `fast-deflate`, else zlib-rs, else miniz_oxide)
/// 2. flate2 (zlib-ng with `fast-deflate`, otherwise miniz_oxide)
pub fn compress(data: &[u8], level: u32) -> Result<Vec<u8>, String> {
#[cfg(all(target_os = "macos", feature = "apple-compression"))]
{
@@ -430,19 +377,9 @@ pub fn active_backend() -> &'static str {
{
"zlib-ng"
}
// flate2 prefers a C zlib over zlib-rs when both are enabled.
#[cfg(all(
not(all(target_os = "macos", feature = "apple-compression")),
not(feature = "fast-deflate"),
feature = "zlib-rs"
))]
{
"zlib-rs"
}
#[cfg(not(any(
all(target_os = "macos", feature = "apple-compression"),
feature = "fast-deflate",
feature = "zlib-rs"
feature = "fast-deflate"
)))]
{
"miniz_oxide"
@@ -499,7 +436,7 @@ mod tests {
fn backend_name_is_set() {
let name = active_backend();
assert!(
["miniz_oxide", "zlib-rs", "zlib-ng", "apple-compression"].contains(&name),
["miniz_oxide", "zlib-ng", "apple-compression"].contains(&name),
"unexpected backend: {name}"
);
}
crates/clawhdf5-filters/src/lib.rs
+4 -6
View File
@@ -2,14 +2,12 @@
//!
//! Provides deflate (zlib) decompression/compression with multiple backend options:
//!
//! - **Default (`zlib-rs` feature)**: `zlib-rs` via flate2 (pure Rust, no C
//! dependencies)
//! - **`fast-deflate` feature**: `zlib-ng` via flate2 (C, built with cmake)
//! - **Default**: `miniz_oxide` (pure Rust, no C dependencies)
//! - **`fast-deflate` feature**: `zlib-ng` via flate2 (~2-3x faster, matches C HDF5)
//! - **`apple-compression` feature**: Apple Compression Framework on macOS
//! (hardware-accelerated on Apple Silicon)
//! - With none of the above: `miniz_oxide` (pure Rust, slower)
//!
//! Backend priority: apple-compression > zlib-ng > zlib-rs > miniz_oxide.
//! Backend priority: apple-compression > zlib-ng > miniz_oxide.
pub mod fast_deflate;
@@ -117,7 +115,7 @@ mod tests {
fn backend_reports_name() {
let name = deflate_backend();
assert!(
["miniz_oxide", "zlib-rs", "zlib-ng", "apple-compression"].contains(&name),
["miniz_oxide", "zlib-ng", "apple-compression"].contains(&name),
"unexpected backend: {name}"
);
}
crates/clawhdf5-format/Cargo.toml
+5 -13
View File
@@ -1,11 +1,10 @@
[package]
name = "clawhdf5-format"
version = "2.7.0"
version = "2.1.0"
edition = "2024"
rust-version.workspace = true
description = "Pure-Rust HDF5 binary format parsing and writing — no C dependencies"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
repository = "https://github.com/redclawsystems/clawhdf5"
readme = "README.md"
keywords = ["hdf5", "science", "data", "binary", "no-std"]
categories = ["parser-implementations", "science", "encoding", "no-std"]
@@ -24,20 +23,16 @@ libaec-sys = { path = "../libaec-sys", version = "0.1", optional = true }
pco = { version = "1.0", optional = true }
[dev-dependencies]
half = { workspace = true }
serde_json = "1"
criterion = { workspace = true }
clawhdf5-derive = { path = "../clawhdf5-derive", version = "2.7.0" }
clawhdf5-derive = { path = "../clawhdf5-derive", version = "2.1.0" }
[[bench]]
name = "bench"
harness = false
[features]
# Deflate backend: `zlib-rs` (pure Rust) by default. `fast-deflate` selects
# zlib-ng instead (C, built with cmake); flate2 prefers a C zlib whenever one
# is enabled, so turning it on anywhere in the build overrides the default.
default = ["std", "checksum", "deflate", "provenance", "zlib-rs", "system-zlib-decompress"]
default = ["std", "checksum", "deflate", "provenance", "fast-deflate", "system-zlib-decompress"]
std = []
checksum = []
deflate = ["flate2"]
@@ -47,10 +42,7 @@ fast-checksum = ["crc32fast"]
fast-deflate = ["flate2/zlib-ng"]
system-zlib = ["flate2/zlib-default"]
system-zlib-decompress = []
# `runtime_detection` gives zlib-rs `std`, which it needs to detect and use
# SIMD at runtime. flate2 enables it by default, but we build flate2 with
# default-features = false, and without it zlib-rs inflates 3.5x slower.
zlib-rs = ["flate2/zlib-rs", "flate2/runtime_detection"]
zlib-rs = ["flate2/zlib-rs"]
lz4 = ["lz4_flex"]
zstd = ["dep:zstd"]
blake3_hash = ["blake3"]
crates/clawhdf5-format/fuzz/.gitignore
-3
View File
@@ -1,4 +1 @@
target/
corpus/
artifacts/
coverage/
crates/clawhdf5-format/fuzz/corpus/fuzz_btree_v2/regression-crash-f98c19dc
BIN
View File
Binary file not shown.
crates/clawhdf5-format/fuzz/corpus/fuzz_datatype/seed_complex_f64_hdf5_2_0
BIN
View File
Binary file not shown.
crates/clawhdf5-format/fuzz/corpus/fuzz_datatype/seed_compound_v1_default_libver
BIN
View File
Binary file not shown.
crates/clawhdf5-format/fuzz/fuzz_targets/fuzz_btree_v2.rs
+6 -27
View File
@@ -1,36 +1,15 @@
#![no_main]
use clawhdf5_format::btree_v2::{BTreeV2Header, collect_btree_v2_records};
use libfuzzer_sys::fuzz_target;
fuzz_target!(|data: &[u8]| {
for &offset_size in &[4u8, 8] {
for &length_size in &[4u8, 8] {
if let Ok(header) = BTreeV2Header::parse(data, 0, offset_size, length_size) {
let _ = collect_btree_v2_records(data, &header, offset_size, length_size);
let _ = clawhdf5_format::btree_v2::BTreeV2Header::parse(
data,
0,
offset_size,
length_size,
);
}
}
}
// Parsing a header requires a valid checksum, which random input almost
// never has, so the traversal behind it went unfuzzed — and that is where
// a node listing itself as its own child overflowed the stack. Take the
// header fields straight from the input instead and walk the rest.
let Some((fields, file)) = data.split_first_chunk::<20>() else {
return;
};
let header = BTreeV2Header {
tree_type: fields[0],
node_size: u32::from_le_bytes([fields[1], fields[2], fields[3], fields[4]]),
record_size: u16::from_le_bytes([fields[5], fields[6]]),
depth: u16::from_le_bytes([fields[7], fields[8]]),
root_node_address: u64::from(u32::from_le_bytes([
fields[9], fields[10], fields[11], fields[12],
])),
num_records_in_root: u16::from_le_bytes([fields[13], fields[14]]),
total_records: u64::from(u32::from_le_bytes([
fields[15], fields[16], fields[17], fields[18],
])),
};
let offset_size = if fields[19] & 1 == 0 { 4 } else { 8 };
let _ = collect_btree_v2_records(file, &header, offset_size, 8);
});
crates/clawhdf5-format/src/attribute.rs
+37 -186
View File
@@ -1,9 +1,7 @@
//! HDF5 Attribute message parsing (message type 0x000C).
#[cfg(not(feature = "std"))]
use alloc::{borrow::Cow, string::String, vec::Vec};
#[cfg(feature = "std")]
use std::borrow::Cow;
use alloc::{string::String, vec::Vec};
use crate::attribute_info::AttributeInfoMessage;
use crate::btree_v2::{BTreeV2Header, collect_btree_v2_records};
@@ -50,64 +48,17 @@ impl AttributeMessage {
///
/// `length_size` is needed for dataspace dimension parsing.
pub fn parse(data: &[u8], length_size: u8) -> Result<AttributeMessage, FormatError> {
Self::parse_impl(data, length_size, None)
}
/// [`AttributeMessage::parse`] with access to the rest of the file, which
/// is needed when the attribute's datatype or dataspace is *shared* (v2/v3
/// flag bits 0/1) — e.g. an attribute created with a committed datatype.
/// In that case the embedded bytes are a reference to the real message,
/// not the message. Without file access such an attribute is an error
/// rather than a garbage datatype.
pub fn parse_in_file(
data: &[u8],
file_data: &[u8],
offset_size: u8,
length_size: u8,
) -> Result<AttributeMessage, FormatError> {
Self::parse_impl(data, length_size, Some((file_data, offset_size)))
}
fn parse_impl(
data: &[u8],
length_size: u8,
file: Option<(&[u8], u8)>,
) -> Result<AttributeMessage, FormatError> {
ensure_len(data, 0, 2)?;
let version = data[0];
match version {
1 => Self::parse_v1(data, length_size),
2 => Self::parse_v2(data, length_size, file),
3 => Self::parse_v3(data, length_size, file),
2 => Self::parse_v2(data, length_size),
3 => Self::parse_v3(data, length_size),
_ => Err(FormatError::InvalidAttributeVersion(version)),
}
}
/// The bytes of an embedded datatype/dataspace message, following the
/// shared-message reference when `shared` is set.
fn embedded_message<'a>(
bytes: &'a [u8],
shared: bool,
msg_type: MessageType,
length_size: u8,
file: Option<(&[u8], u8)>,
) -> Result<Cow<'a, [u8]>, FormatError> {
if !shared {
return Ok(Cow::Borrowed(bytes));
}
let (file_data, offset_size) = file.ok_or(FormatError::UnresolvedSharedMessage)?;
let shared_ref = shared_message::parse_shared_ref_sized(bytes, offset_size, length_size)?;
shared_message::resolve_shared_message(
file_data,
&shared_ref,
msg_type,
offset_size,
length_size,
)
.map(Cow::Owned)
}
fn parse_v1(data: &[u8], length_size: u8) -> Result<AttributeMessage, FormatError> {
// version(1) + reserved(1) + name_size(2) + datatype_size(2) + dataspace_size(2) = 8
ensure_len(data, 0, 8)?;
@@ -143,13 +94,7 @@ impl AttributeMessage {
})
}
fn parse_v2(
data: &[u8],
length_size: u8,
file: Option<(&[u8], u8)>,
) -> Result<AttributeMessage, FormatError> {
// Flags: bit 0 = datatype is shared, bit 1 = dataspace is shared.
let flags = data.get(1).copied().unwrap_or(0);
fn parse_v2(data: &[u8], length_size: u8) -> Result<AttributeMessage, FormatError> {
// version(1) + flags(1) + name_size(2) + datatype_size(2) + dataspace_size(2) = 8
ensure_len(data, 0, 8)?;
let name_size = u16::from_le_bytes([data[2], data[3]]) as usize;
@@ -165,26 +110,12 @@ impl AttributeMessage {
// Datatype (NO padding)
ensure_len(data, pos, datatype_size)?;
let dt_bytes = Self::embedded_message(
&data[pos..pos + datatype_size],
flags & 0x01 != 0,
MessageType::Datatype,
length_size,
file,
)?;
let (datatype, _) = Datatype::parse(&dt_bytes)?;
let (datatype, _) = Datatype::parse(&data[pos..pos + datatype_size])?;
pos += datatype_size;
// Dataspace (NO padding)
ensure_len(data, pos, dataspace_size)?;
let ds_bytes = Self::embedded_message(
&data[pos..pos + dataspace_size],
flags & 0x02 != 0,
MessageType::Dataspace,
length_size,
file,
)?;
let dataspace = Dataspace::parse(&ds_bytes, length_size)?;
let dataspace = Dataspace::parse(&data[pos..pos + dataspace_size], length_size)?;
pos += dataspace_size;
let raw_data = compute_raw_data(data, pos, &dataspace, &datatype);
@@ -197,13 +128,7 @@ impl AttributeMessage {
})
}
fn parse_v3(
data: &[u8],
length_size: u8,
file: Option<(&[u8], u8)>,
) -> Result<AttributeMessage, FormatError> {
// Flags: bit 0 = datatype is shared, bit 1 = dataspace is shared.
let flags = data.get(1).copied().unwrap_or(0);
fn parse_v3(data: &[u8], length_size: u8) -> Result<AttributeMessage, FormatError> {
// version(1) + flags(1) + name_size(2) + datatype_size(2) + dataspace_size(2) + encoding(1) = 9
ensure_len(data, 0, 9)?;
let name_size = u16::from_le_bytes([data[2], data[3]]) as usize;
@@ -220,26 +145,12 @@ impl AttributeMessage {
// Datatype (NO padding)
ensure_len(data, pos, datatype_size)?;
let dt_bytes = Self::embedded_message(
&data[pos..pos + datatype_size],
flags & 0x01 != 0,
MessageType::Datatype,
length_size,
file,
)?;
let (datatype, _) = Datatype::parse(&dt_bytes)?;
let (datatype, _) = Datatype::parse(&data[pos..pos + datatype_size])?;
pos += datatype_size;
// Dataspace (NO padding)
ensure_len(data, pos, dataspace_size)?;
let ds_bytes = Self::embedded_message(
&data[pos..pos + dataspace_size],
flags & 0x02 != 0,
MessageType::Dataspace,
length_size,
file,
)?;
let dataspace = Dataspace::parse(&ds_bytes, length_size)?;
let dataspace = Dataspace::parse(&data[pos..pos + dataspace_size], length_size)?;
pos += dataspace_size;
let raw_data = compute_raw_data(data, pos, &dataspace, &datatype);
@@ -394,80 +305,32 @@ pub fn find_attribute<'a>(
///
/// Use this instead of `extract_attributes` when reading files that may use dense storage
/// (e.g., objects with many attributes, typically >8).
///
/// Fails if any attribute cannot be read; see [`extract_attributes_tolerant`]
/// to read the others.
pub fn extract_attributes_full(
file_data: &[u8],
header: &ObjectHeader,
offset_size: u8,
length_size: u8,
) -> Result<Vec<AttributeMessage>, FormatError> {
extract_attributes_with(file_data, header, offset_size, length_size, &mut Err)
}
/// Like [`extract_attributes_full`], but an attribute that cannot be read
/// (a corrupt or unsupported attribute message, or a heap object that cannot
/// be located) is left out and its error returned alongside the attributes
/// that could be read, instead of failing them all.
///
/// Errors in the structures that index the attributes (the Attribute Info
/// message, the dense-storage heap header or B-tree) still fail the call:
/// then it is unknown which attributes exist at all.
pub fn extract_attributes_tolerant(
file_data: &[u8],
header: &ObjectHeader,
offset_size: u8,
length_size: u8,
) -> Result<(Vec<AttributeMessage>, Vec<FormatError>), FormatError> {
let mut errors = Vec::new();
let attrs = extract_attributes_with(file_data, header, offset_size, length_size, &mut |e| {
errors.push(e);
Ok(())
})?;
Ok((attrs, errors))
}
/// Read every attribute; each one that fails goes to `on_error`, which
/// either stops the read (returns the error) or skips that attribute.
fn extract_attributes_with(
file_data: &[u8],
header: &ObjectHeader,
offset_size: u8,
length_size: u8,
on_error: &mut dyn FnMut(FormatError) -> Result<(), FormatError>,
) -> Result<Vec<AttributeMessage>, FormatError> {
let mut attrs = Vec::new();
// Collect compact attributes (inline in OH)
for msg in &header.messages {
if msg.msg_type == MessageType::Attribute {
let attr = if shared_message::is_shared(msg.flags) {
if shared_message::is_shared(msg.flags) {
// Shared attribute: resolve the reference to get actual attribute data
shared_message::parse_shared_ref_sized(&msg.data, offset_size, length_size)
.and_then(|shared_ref| {
shared_message::resolve_shared_message(
let shared_ref = shared_message::parse_shared_ref(&msg.data, offset_size)?;
let resolved_data = shared_message::resolve_shared_message(
file_data,
&shared_ref,
MessageType::Attribute,
offset_size,
length_size,
)
})
.and_then(|resolved| {
AttributeMessage::parse_in_file(
&resolved,
file_data,
offset_size,
length_size,
)
})
)?;
let attr = AttributeMessage::parse(&resolved_data, length_size)?;
attrs.push(attr);
} else {
AttributeMessage::parse_in_file(&msg.data, file_data, offset_size, length_size)
};
match attr {
Ok(attr) => attrs.push(attr),
Err(e) => on_error(e)?,
let attr = AttributeMessage::parse(&msg.data, length_size)?;
attrs.push(attr);
}
}
}
@@ -477,15 +340,9 @@ fn extract_attributes_with(
if let Some(info) = attr_info
&& let Some(fh_addr) = info.fractal_heap_address
{
extract_dense_attributes(
file_data,
&info,
fh_addr,
offset_size,
length_size,
&mut attrs,
on_error,
)?;
let dense_attrs =
extract_dense_attributes(file_data, &info, fh_addr, offset_size, length_size)?;
attrs.extend(dense_attrs);
}
Ok(attrs)
@@ -512,9 +369,7 @@ fn extract_dense_attributes(
fh_addr: u64,
offset_size: u8,
length_size: u8,
attrs: &mut Vec<AttributeMessage>,
on_error: &mut dyn FnMut(FormatError) -> Result<(), FormatError>,
) -> Result<(), FormatError> {
) -> Result<Vec<AttributeMessage>, FormatError> {
// Parse fractal heap
let fh = FractalHeapHeader::parse(file_data, fh_addr as usize, offset_size, length_size)?;
@@ -528,32 +383,27 @@ fn extract_dense_attributes(
let btree_hdr = BTreeV2Header::parse(file_data, btree_addr as usize, offset_size, length_size)?;
let records = collect_btree_v2_records(file_data, &btree_hdr, offset_size, length_size)?;
let mut attrs = Vec::new();
for record in &records {
// Per HDF5 spec, both type 8 and type 9 records start with heap_id:
// Type 8: heap_id(8) + msg_flags(1) + creation_order(4) + hash(4)
// Type 9: heap_id(8) + msg_flags(1) + creation_order(4)
let id_len = fh.heap_id_length as usize;
let Some(id_bytes) = record.data.get(..id_len) else {
on_error(FormatError::UnexpectedEof {
expected: id_len,
available: record.data.len(),
})?;
let id_offset = 0;
if record.data.len() < id_offset + fh.heap_id_length as usize {
continue;
};
}
let id_bytes = &record.data[id_offset..id_offset + fh.heap_id_length as usize];
// Read attribute message from fractal heap
let attr_data = fh.read_managed_object(file_data, id_bytes, offset_size)?;
// The data in the heap is a complete attribute message
let attr = fh
.read_managed_object(file_data, id_bytes, offset_size)
.and_then(|attr_data| {
AttributeMessage::parse_in_file(&attr_data, file_data, offset_size, length_size)
});
match attr {
Ok(attr) => attrs.push(attr),
Err(e) => on_error(e)?,
}
let attr = AttributeMessage::parse(&attr_data, length_size)?;
attrs.push(attr);
}
Ok(())
Ok(attrs)
}
#[cfg(test)]
@@ -622,13 +472,14 @@ mod tests {
// Name padded to 8 bytes
data.extend_from_slice(name);
if data.len() % 8 != 0 || data.len() == 8 {
while data.len() % 8 != 0 || data.len() == 8 {
// Pad name to 8-byte boundary from start of name
let name_start = 8;
let name_padded = pad8(name_size);
while data.len() < name_start + name_padded {
data.push(0);
}
break;
}
// Datatype padded to 8 bytes
@@ -898,11 +749,11 @@ mod tests {
data.extend_from_slice(name);
data.extend_from_slice(&dt_bytes);
data.extend_from_slice(&ds_bytes);
data.extend_from_slice(&3.25f64.to_le_bytes());
data.extend_from_slice(&3.14f64.to_le_bytes());
let attr = AttributeMessage::parse(&data, 8).unwrap();
let vals = attr.read_as_f64().unwrap();
assert_eq!(vals, vec![3.25]);
assert_eq!(vals, vec![3.14]);
}
#[test]
crates/clawhdf5-format/src/btree_v2.rs
+38 -214
View File
@@ -172,17 +172,6 @@ fn max_records_leaf(node_size: u32, record_size: u16) -> u64 {
((node_size - overhead) / record_size as u32) as u64
}
/// Deepest B-tree v2 accepted. See [`collect_btree_v2_records`].
const MAX_DEPTH: u16 = 64;
/// Take `n` records from the traversal's budget, or refuse the tree.
fn spend(budget: &mut usize, n: usize) -> Result<(), FormatError> {
*budget = budget
.checked_sub(n)
.ok_or(FormatError::NestingDepthExceeded)?;
Ok(())
}
/// Collect all records from a B-tree v2 by traversing from the root.
pub fn collect_btree_v2_records(
file_data: &[u8],
@@ -193,22 +182,6 @@ pub fn collect_btree_v2_records(
if header.total_records == 0 || header.num_records_in_root == 0 {
return Ok(Vec::new());
}
// Recursion is one frame per level, and the depth is read from the file:
// a crafted header claiming 65 535 levels over a node that is its own
// child overflowed the stack. 64 matches the fractal heap's guard, and no
// real tree comes close — even at the minimum fan-out of two it would
// hold more than 2^64 records.
if header.depth > MAX_DEPTH {
return Err(FormatError::NestingDepthExceeded);
}
// A valid tree stores each record once, in its own bytes, so it cannot
// hold more records than the file has room for. Children are addresses,
// though, and nothing makes them distinct: levels whose children all
// point at one shared node below reach it fan-out^depth times, which is
// millions of records from a few kilobytes. Counting against what the
// file could physically contain bounds that without trusting the
// header's own `total_records`.
let mut budget = file_data.len() / usize::from(header.record_size.max(1));
let max_leaf_nrec = max_records_leaf(header.node_size, header.record_size);
@@ -233,7 +206,6 @@ pub fn collect_btree_v2_records(
offset_size,
length_size,
max_leaf_nrec,
&mut budget,
&mut records,
)?;
Ok(records)
@@ -301,7 +273,6 @@ fn collect_internal_records(
offset_size: u8,
length_size: u8,
max_leaf_nrec: u64,
budget: &mut usize,
out: &mut Vec<BTreeV2Record>,
) -> Result<(), FormatError> {
// signature(4) + version(1) + type(1) = 6
@@ -323,21 +294,39 @@ fn collect_internal_records(
let records_start = pos;
pos += records_total;
// Child pointer layout, as libhdf5 computes it (H5B2__hdr_init): the
// child's record count is always encoded in the width needed for a
// *leaf's* maximum, and — below the first internal level — the child
// subtree's total record count in the width needed for the most records
// a subtree of that depth can hold.
// Compute sizes for child pointers
// max_records at child depth - for variable-width nrec encoding
let child_depth = depth - 1;
let nrec_width = bytes_for_max_records(max_leaf_nrec);
let max_nrec_child = if child_depth == 0 {
max_leaf_nrec
} else {
// For internal nodes at child_depth, the true max_nrec depends on the
// node size, record size, and the recursive width of child pointer
// entries (which themselves depend on max_nrec at deeper levels).
// Computing the exact value requires iterating from the leaf level
// upward, as described in the HDF5 spec (III.A.2 "Computing the Size
// of B-tree Nodes").
//
// We use `max_leaf_nrec * 2` as a conservative upper bound. This
// over-estimates the nrec encoding width, which means we may read
// slightly more bytes per child pointer than strictly necessary, but
// never fewer. The over-read bytes are harmless because we only
// decode `num_records` entries (the actual count from the node header).
//
// Known limitation: for very deep trees (depth > 3) with small record
// sizes, the true max could exceed this estimate, causing us to
// under-allocate the nrec encoding width and misparse child pointers.
// In practice, HDF5 B-tree v2 depths rarely exceed 2-3.
max_leaf_nrec * 2
};
let nrec_width = bytes_for_max_records(max_nrec_child);
// Total records in subtree width (only if depth > 1)
let total_nrec_width = if depth > 1 {
bytes_for_max_records(cum_max_records(
node_size,
record_size,
offset_size,
max_leaf_nrec,
child_depth,
))
// Width to hold total records in a subtree
// We compute max possible total records at this subtree depth
let max_total = header_max_total_records(max_leaf_nrec, depth - 1);
bytes_for_max_records(max_total)
} else {
0
};
@@ -361,8 +350,6 @@ fn collect_internal_records(
// We collect child[0] records, then record[0], then child[1], etc.
for (i, &(child_addr, child_nrec)) in children.iter().enumerate() {
if child_depth == 0 {
// Before parsing, so a refused tree is not also a large allocation.
spend(budget, usize::from(child_nrec))?;
let leaf_recs =
parse_leaf_records(file_data, child_addr as usize, child_nrec, record_size)?;
out.extend(leaf_recs);
@@ -377,7 +364,6 @@ fn collect_internal_records(
offset_size,
length_size,
max_leaf_nrec,
budget,
out,
)?;
}
@@ -407,7 +393,6 @@ fn collect_internal_records(
available: file_data.len(),
});
}
spend(budget, 1)?;
out.push(BTreeV2Record {
data: file_data[rec_start..rec_end].to_vec(),
});
@@ -417,43 +402,20 @@ fn collect_internal_records(
Ok(())
}
/// Most records a subtree whose root is at `depth` can hold (libhdf5's
/// `cum_max_nrec`): a leaf holds `max_leaf_nrec`; an internal node at depth
/// `d` holds `max_nrec(d)` records and `max_nrec(d) + 1` subtrees of depth
/// `d - 1`, where `max_nrec(d)` is what fits in a node once each record is
/// paired with a child pointer of the width depth `d` needs.
fn cum_max_records(
node_size: u32,
record_size: u16,
offset_size: u8,
max_leaf_nrec: u64,
depth: u16,
) -> u64 {
// Internal node overhead: signature(4) + version(1) + type(1) + checksum(4).
const PREFIX: u64 = 10;
let nrec_width = bytes_for_max_records(max_leaf_nrec) as u64;
let mut cum = max_leaf_nrec;
let mut cum_width = 0u64;
for d in 1..=depth {
let ptr = u64::from(offset_size) + nrec_width + if d > 1 { cum_width } else { 0 };
let max_nrec = u64::from(node_size)
.saturating_sub(PREFIX)
.saturating_sub(ptr)
/ (u64::from(record_size) + ptr).max(1);
cum = max_nrec
.saturating_add(1)
.saturating_mul(cum)
.saturating_add(max_nrec);
cum_width = bytes_for_max_records(cum) as u64;
/// Estimate maximum total records at a given depth (for variable-width encoding).
fn header_max_total_records(max_leaf_nrec: u64, depth: u16) -> u64 {
// Conservative: branching factor * max_leaf at each level
let mut total = max_leaf_nrec;
for _ in 0..depth {
total = total.saturating_mul(max_leaf_nrec.max(2));
}
cum
total
}
#[cfg(test)]
mod tests {
use super::*;
#[allow(clippy::too_many_arguments)]
fn build_btree_v2_header(
tree_type: u8,
node_size: u32,
@@ -503,130 +465,6 @@ mod tests {
buf
}
/// An internal node laid out exactly as `collect_internal_records` will
/// read it at `depth`: `records` zeroed records, then `children` pointers,
/// all to `child_addr` claiming `child_nrec` records.
fn internal_node(
depth: u16,
node_size: u32,
record_size: u16,
records: usize,
children: usize,
child_addr: u64,
child_nrec: u64,
) -> Vec<u8> {
let max_leaf = max_records_leaf(node_size, record_size);
let nrec_width = bytes_for_max_records(max_leaf);
let total_width = if depth > 1 {
bytes_for_max_records(cum_max_records(
node_size,
record_size,
8,
max_leaf,
depth - 1,
))
} else {
0
};
let mut buf = b"BTIN".to_vec();
buf.extend_from_slice(&[0, 5]);
buf.resize(buf.len() + records * record_size as usize, 0);
for _ in 0..children {
buf.extend_from_slice(&child_addr.to_le_bytes());
buf.extend_from_slice(&child_nrec.to_le_bytes()[..nrec_width]);
buf.resize(buf.len() + total_width, 0);
}
buf
}
fn header(depth: u16, root: u64, root_nrec: u16, total: u64) -> BTreeV2Header {
BTreeV2Header {
tree_type: 5,
node_size: 512,
record_size: 8,
depth,
root_node_address: root,
num_records_in_root: root_nrec,
total_records: total,
}
}
#[test]
fn a_node_that_is_its_own_child_is_rejected_not_recursed() {
// One internal node whose two children are itself, under a header
// claiming the deepest tree a u16 allows. The layout stops depending
// on depth once the subtree-total width saturates, so every level
// parses cleanly and recursion runs ~65 000 frames deep: before the
// cap this overflowed the stack and aborted the process, from a file
// of under 100 bytes.
let mut data = internal_node(u16::MAX, 512, 8, 1, 2, 0, 1);
data.resize(4096, 0);
let result = collect_btree_v2_records(&data, &header(u16::MAX, 0, 1, 1), 8, 8);
assert!(result.is_err(), "{result:?}");
}
#[test]
fn a_shared_subtree_cannot_multiply_the_work() {
// A chain of distinct levels, each node's children all pointing at the
// single node below, ending in a real leaf. Every node parses and
// nothing is cyclic, yet the leaf is reached fan-out^depth times: 62
// children over 4 levels is ~15 million leaf visits from a few
// kilobytes. A valid tree cannot hold more records than the file has
// room for, so that bounds the traversal instead.
let (node_size, record_size) = (512u32, 8u16);
let fanout = 62usize;
let depth = 4u16;
let leaf = build_leaf_node(5, &[&[0u8; 8][..]]);
// Lay out root first, then each lower level, then the leaf.
let mut nodes: Vec<Vec<u8>> = Vec::new();
let mut addrs = Vec::new();
let mut at = 0u64;
let mut sizes = Vec::new();
for d in (1..=depth).rev() {
let n = internal_node(d, node_size, record_size, fanout - 1, fanout, 0, 0);
sizes.push(n.len());
}
for size in &sizes {
addrs.push(at);
at += *size as u64;
}
let leaf_addr = at;
for (i, d) in (1..=depth).rev().enumerate() {
let (child, child_nrec) = if d == 1 {
(leaf_addr, 1)
} else {
(addrs[i + 1], fanout as u64 - 1)
};
nodes.push(internal_node(
d,
node_size,
record_size,
fanout - 1,
fanout,
child,
child_nrec,
));
}
let mut data: Vec<u8> = nodes.concat();
data.extend_from_slice(&leaf);
data.resize(data.len() + 64, 0);
let started = std::time::Instant::now();
let result =
collect_btree_v2_records(&data, &header(depth, 0, fanout as u16 - 1, u64::MAX), 8, 8);
assert!(
result.is_err(),
"expected a refusal, got {} records",
result.map_or(0, |r| r.len())
);
assert!(
started.elapsed() < std::time::Duration::from_secs(2),
"took {:?}",
started.elapsed()
);
}
#[test]
fn parse_header() {
let data = build_btree_v2_header(5, 512, 11, 0, 0x1000, 3, 3, 8, 8);
@@ -683,18 +521,4 @@ mod tests {
let records = collect_btree_v2_records(&header, &hdr, 8, 8).unwrap();
assert!(records.is_empty());
}
#[test]
fn subtree_capacity_matches_libhdf5() {
// A link-name index (11-byte records, 512-byte nodes, 8-byte
// addresses): libhdf5's H5B2__hdr_init gives 45 records per leaf,
// then cum_max_nrec 1 149 at depth 1 and 26 449 at depth 2 — two
// bytes of subtree count in a depth-3 root's child pointers, where
// leaf_max^3 = 91 125 would need three.
let leaf = max_records_leaf(512, 11);
assert_eq!(leaf, 45);
assert_eq!(cum_max_records(512, 11, 8, leaf, 0), 45);
assert_eq!(cum_max_records(512, 11, 8, leaf, 1), 1_149);
assert_eq!(cum_max_records(512, 11, 8, leaf, 2), 26_449);
}
}
crates/clawhdf5-format/src/chunk_cache.rs
+276 -526
View File
@@ -223,32 +223,13 @@ pub const DEFAULT_CACHE_BYTES: usize = 16 * 1024 * 1024; // 16 MiB
/// coordinate map and reduces collision chains compared to power-of-two sizes.
pub const DEFAULT_MAX_SLOTS: usize = 521;
/// Most datasets whose chunk index a [`ChunkCache`] keeps at once.
pub const MAX_INDEXED_DATASETS: usize = 64;
/// Most chunk-index entries, summed over all datasets, a [`ChunkCache`] keeps.
/// Least-recently-used datasets' indexes are dropped past this (the dataset
/// being read is always kept), so a file with many or huge chunked datasets
/// cannot grow the cache without bound.
pub const MAX_INDEXED_CHUNKS: usize = 1 << 20;
/// The dataset key the address-less (legacy) methods use when
/// [`ChunkCache::ensure_dataset`] has not been called.
#[cfg(feature = "std")]
const UNBOUND_DATASET: u64 = u64::MAX;
// ---------------------------------------------------------------------------
// LRU entry
// ---------------------------------------------------------------------------
/// Decompressed chunks are keyed by dataset *and* coordinate: every chunked
/// dataset has a chunk at (0, 0, ...), so the coordinate alone is ambiguous.
#[cfg(feature = "std")]
type SlotKey = (u64, ChunkCoord);
#[cfg(feature = "std")]
struct CachedChunk {
key: SlotKey,
coord: ChunkCoord,
/// Shared so a cache hit is a refcount bump, not a copy of the whole
/// (potentially large) decompressed chunk.
data: Arc<CacheAlignedBuffer>,
@@ -256,48 +237,21 @@ struct CachedChunk {
last_access: u64,
}
/// Per-dataset index state.
#[cfg(feature = "std")]
#[derive(Default)]
struct DatasetEntry {
/// Chunk coordinate -> ChunkInfo (offset + size in file).
index: Option<Arc<HashMap<ChunkCoord, ChunkInfo>>>,
/// Pre-built chunk index for O(1) coordinate lookups.
chunk_index: Option<Arc<ChunkIndex>>,
/// Pre-computed chunk layout for fast assembly.
chunk_layout: Option<Arc<ChunkLayout>>,
/// Tick of the last use, for dropping the least recently used dataset.
last_used: u64,
}
#[cfg(feature = "std")]
impl DatasetEntry {
fn weight(&self) -> usize {
self.index.as_ref().map_or(0, |m| m.len())
+ self.chunk_index.as_ref().map_or(0, |c| c.num_chunks())
}
}
// ---------------------------------------------------------------------------
// ChunkCache
// ---------------------------------------------------------------------------
/// A per-file chunk cache: chunk indexes per dataset, plus an LRU of
/// decompressed chunks, all keyed by dataset.
/// A per-dataset chunk cache with hash-based index and LRU eviction.
///
/// A dataset is identified by the address of its chunk index (B-tree, fixed
/// or extensible array, ...), which is unique within a file. Every method
/// that takes an `addr` works on that dataset only, so threads reading
/// different datasets through one shared cache never see each other's
/// chunks. The address-less methods (`has_index`, `populate_index`,
/// `get_decompressed`, ...) act on the dataset last bound with
/// [`Self::ensure_dataset`]; that binding is shared state, so concurrent
/// readers must use the `*_in` / `*_for` methods instead (the chunked
/// readers in [`crate::chunked_read`] do).
/// # Usage
///
/// Memory is bounded: decompressed data by `max_bytes`/`max_slots` across
/// all datasets, indexes by [`MAX_INDEXED_DATASETS`] and
/// [`MAX_INDEXED_CHUNKS`].
/// ```ignore
/// let cache = ChunkCache::new();
/// // Pass &cache to read_chunked_data — it will populate the index lazily.
/// ```
///
/// The cache is wrapped in `Mutex` internally so it can be mutated through
/// shared references (thread-safe).
///
/// Only available with the `std` feature because it requires `std::sync::Mutex`.
#[cfg(feature = "std")]
@@ -307,20 +261,26 @@ pub struct ChunkCache {
#[cfg(feature = "std")]
struct CacheInner {
/// Per-dataset chunk indexes, keyed by chunk-index address.
datasets: HashMap<u64, DatasetEntry>,
/// Hash index: chunk coordinate -> ChunkInfo (offset + size in file).
/// Populated once per dataset on first access.
index: Option<HashMap<ChunkCoord, ChunkInfo>>,
/// Dataset the address-less methods act on (see `ensure_dataset`).
current: Option<u64>,
/// Address of the dataset (its chunk-index base address) that the cached
/// index, chunk index, layout, and decompressed slots currently belong to.
/// The cache is shared per file across datasets, so every cached-read entry
/// checks this and resets the per-dataset state when the dataset changes —
/// otherwise one dataset's chunk index (with its own rank) would be reused
/// for another, corrupting reads.
index_addr: Option<u64>,
/// LRU cache of decompressed chunk data.
slots: Vec<CachedChunk>,
/// Key -> index into `slots`, for O(1) lookup instead of a linear
/// Coordinate -> index into `slots`, for O(1) lookup instead of a linear
/// scan. Kept in sync with `slots` on every insert/evict/clear — in
/// particular, `slots.swap_remove(i)` moves the last element into slot
/// `i`, so the moved element's index entry must be updated too.
slot_index: HashMap<SlotKey, usize>,
slot_index: HashMap<ChunkCoord, usize>,
/// Current total bytes of cached decompressed data.
current_bytes: usize,
@@ -334,145 +294,17 @@ struct CacheInner {
/// Monotonic counter for LRU ordering.
tick: u64,
/// Last accessed chunk (for sequential detection).
last_coord: Option<SlotKey>,
/// Last accessed chunk coordinate (for sequential detection).
last_coord: Option<ChunkCoord>,
/// Access pattern statistics.
stats: AccessStats,
}
#[cfg(feature = "std")]
impl CacheInner {
fn current(&self) -> u64 {
self.current.unwrap_or(UNBOUND_DATASET)
}
/// Pre-built chunk index for O(1) coordinate lookups.
chunk_index: Option<ChunkIndex>,
fn touch(&mut self, addr: u64) -> &mut DatasetEntry {
self.tick += 1;
let tick = self.tick;
let entry = self.datasets.entry(addr).or_default();
entry.last_used = tick;
entry
}
fn entry(&self, addr: u64) -> Option<&DatasetEntry> {
self.datasets.get(&addr)
}
/// Drop least-recently-used datasets' indexes (never `keep`'s) until the
/// dataset and chunk-entry budgets hold.
fn trim_datasets(&mut self, keep: u64) {
loop {
let total: usize = self.datasets.values().map(DatasetEntry::weight).sum();
if self.datasets.len() <= MAX_INDEXED_DATASETS && total <= MAX_INDEXED_CHUNKS {
return;
}
let victim = self
.datasets
.iter()
.filter(|(a, _)| **a != keep)
.min_by_key(|(_, e)| e.last_used)
.map(|(a, _)| *a);
match victim {
Some(a) => {
self.datasets.remove(&a);
}
None => return,
}
}
}
fn get_decompressed(&mut self, addr: u64, coord: &[u64]) -> Option<Arc<CacheAlignedBuffer>> {
self.tick += 1;
let tick = self.tick;
// Track sequential vs random access
let is_sequential = self.last_coord.as_ref().is_some_and(|(prev_addr, prev)| {
// Sequential if exactly one dimension changed
let changes: usize = prev
.iter()
.zip(coord.iter())
.filter(|(a, b)| a != b)
.count();
*prev_addr == addr && changes <= 1
});
if is_sequential {
self.stats.sequential_count += 1;
} else if self.last_coord.is_some() {
self.stats.random_count += 1;
}
let key: SlotKey = (addr, coord.to_vec());
let found = if let Some(&idx) = self.slot_index.get(&key) {
self.slots[idx].last_access = tick;
Some(Arc::clone(&self.slots[idx].data))
} else {
None
};
self.last_coord = Some(key);
if let Some(ref data) = found {
self.stats.hits += 1;
self.stats.bytes_read += data.len() as u64;
} else {
self.stats.misses += 1;
}
found
}
fn put_decompressed(
&mut self,
key: SlotKey,
data: Arc<CacheAlignedBuffer>,
) -> Arc<CacheAlignedBuffer> {
let data_len = data.len();
// Don't cache if single chunk exceeds budget — still return the data
// to the caller, just don't retain it.
if data_len > self.max_bytes {
return data;
}
// Check if already present
self.tick += 1;
let tick = self.tick;
if let Some(&idx) = self.slot_index.get(&key) {
self.slots[idx].last_access = tick;
return Arc::clone(&self.slots[idx].data); // already cached
}
// Evict until we have room
while self.slots.len() >= self.max_slots
|| (self.current_bytes + data_len > self.max_bytes && !self.slots.is_empty())
{
// Find LRU slot
let lru_idx = self
.slots
.iter()
.enumerate()
.min_by_key(|(_, s)| s.last_access)
.map(|(i, _)| i)
.unwrap();
let removed = self.slots.swap_remove(lru_idx);
self.slot_index.remove(&removed.key);
// swap_remove moved the former last element into `lru_idx` (unless
// it *was* the last element) — fix up that element's index entry.
if lru_idx < self.slots.len() {
let moved_key = self.slots[lru_idx].key.clone();
self.slot_index.insert(moved_key, lru_idx);
}
self.current_bytes -= removed.data.len();
self.stats.evictions += 1;
}
self.current_bytes += data_len;
let new_idx = self.slots.len();
self.slot_index.insert(key.clone(), new_idx);
self.slots.push(CachedChunk {
key,
data: Arc::clone(&data),
last_access: tick,
});
data
}
/// Pre-computed chunk layout for fast assembly.
chunk_layout: Option<ChunkLayout>,
}
/// Access pattern statistics tracked by the chunk cache.
@@ -524,8 +356,8 @@ impl ChunkCache {
pub fn with_capacity(max_bytes: usize, max_slots: usize) -> Self {
Self {
inner: std::sync::Mutex::new(CacheInner {
datasets: HashMap::new(),
current: None,
index: None,
index_addr: None,
slots: Vec::with_capacity(max_slots.min(64)),
slot_index: HashMap::with_capacity(max_slots.min(64)),
current_bytes: 0,
@@ -534,331 +366,335 @@ impl ChunkCache {
tick: 0,
last_coord: None,
stats: AccessStats::default(),
chunk_index: None,
chunk_layout: None,
}),
}
}
fn lock(&self) -> std::sync::MutexGuard<'_, CacheInner> {
self.inner.lock().unwrap_or_else(|e| e.into_inner())
}
// ----- Index operations -----
/// The most decompressed bytes this cache will hold.
pub fn max_bytes(&self) -> usize {
self.lock().max_bytes
}
// ----- Dataset-keyed operations (safe to use concurrently) -----
/// The chunk list of the dataset whose chunk index is at `addr`.
/// Bind the cache to the dataset at chunk-index address `addr`.
///
/// On the first call for a dataset, `build` scans its chunk index; the
/// result is kept (offsets truncated to `rank` for the lookup key), so
/// later calls skip the scan. `build` runs without the cache lock held;
/// if two threads race to build the same dataset's index, the first
/// stored one wins and both return equivalent lists.
pub fn chunks_for<E>(
&self,
addr: u64,
rank: usize,
build: impl FnOnce() -> Result<Vec<ChunkInfo>, E>,
) -> Result<Vec<ChunkInfo>, E> {
Ok(self
.index_for(addr, rank, build)?
.values()
.cloned()
.collect())
}
fn index_for<E>(
&self,
addr: u64,
rank: usize,
build: impl FnOnce() -> Result<Vec<ChunkInfo>, E>,
) -> Result<Arc<HashMap<ChunkCoord, ChunkInfo>>, E> {
if let Some(index) = self.lock().touch(addr).index.clone() {
return Ok(index);
}
let chunks = build()?;
let map: HashMap<ChunkCoord, ChunkInfo> = chunks
.into_iter()
.map(|ci| (ci.offsets.iter().take(rank).copied().collect(), ci))
.collect();
let mut inner = self.lock();
let entry = inner.touch(addr);
let index = Arc::clone(entry.index.get_or_insert_with(|| Arc::new(map)));
inner.trim_datasets(addr);
Ok(index)
}
/// The pre-computed assembly layout of the dataset at `addr`, building
/// its chunk index (via `build`, as in [`Self::chunks_for`]) and layout on
/// first use.
pub fn chunk_layout_for<E>(
&self,
addr: u64,
rank: usize,
build: impl FnOnce() -> Result<Vec<ChunkInfo>, E>,
ds_dims: &[usize],
chunk_dims: &[usize],
elem_size: usize,
) -> Result<Arc<ChunkLayout>, E> {
let (layout, chunk_index) = {
let mut inner = self.lock();
let entry = inner.touch(addr);
(entry.chunk_layout.clone(), entry.chunk_index.clone())
};
if let Some(layout) = layout {
return Ok(layout);
}
let chunk_index = match chunk_index {
Some(ci) => ci,
None => {
let index = self.index_for(addr, rank, build)?;
let chunks: Vec<ChunkInfo> = index.values().cloned().collect();
Arc::new(ChunkIndex::build(&chunks, rank))
}
};
let layout = ChunkLayout::build(&chunk_index, ds_dims, chunk_dims, elem_size);
let mut inner = self.lock();
let entry = inner.touch(addr);
entry.chunk_index.get_or_insert(chunk_index);
let layout = Arc::clone(entry.chunk_layout.get_or_insert_with(|| Arc::new(layout)));
inner.trim_datasets(addr);
Ok(layout)
}
/// Cached decompressed chunk at `coord` of the dataset at `addr`.
///
/// O(1) lookup; the clone is an `Arc` refcount bump, not a copy of the
/// underlying decompressed data.
pub fn get_decompressed_in(&self, addr: u64, coord: &[u64]) -> Option<Arc<CacheAlignedBuffer>> {
self.lock().get_decompressed(addr, coord)
}
/// Cache decompressed chunk data for `coord` of the dataset at `addr`.
/// Returns the `Arc`-shared buffer now cached (or already cached).
pub fn put_decompressed_in(
&self,
addr: u64,
coord: ChunkCoord,
data: Vec<u8>,
) -> Arc<CacheAlignedBuffer> {
self.put_decompressed_aligned_in(addr, coord, CacheAlignedBuffer::from_vec(data))
}
/// [`Self::put_decompressed_in`] for an already-aligned buffer.
pub fn put_decompressed_aligned_in(
&self,
addr: u64,
coord: ChunkCoord,
data: CacheAlignedBuffer,
) -> Arc<CacheAlignedBuffer> {
let data = Arc::new(data);
self.lock().put_decompressed((addr, coord), data)
}
/// Record that the given chunk coordinates of the dataset at `addr` are
/// predicted to be accessed soon (bookkeeping only).
///
/// This does **not** prefetch or pre-decompress anything — it only
/// checks whether each coordinate is already in the chunk index and
/// updates access-pattern stats accordingly.
pub fn prefetch_hint_in(&self, addr: u64, next_coords: &[ChunkCoord]) {
let mut inner = self.lock();
let Some(index) = inner.entry(addr).and_then(|e| e.index.clone()) else {
return;
};
let known = next_coords
.iter()
.filter(|c| index.contains_key(*c))
.count();
inner.stats.sequential_count += known as u64;
}
// ----- Address-less operations on the bound dataset -----
/// Bind the address-less methods to the dataset at chunk-index address
/// `addr`. Returns `true` if this changed the bound dataset.
///
/// Each dataset's state is kept separately, so switching loses nothing
/// and never exposes one dataset's index or chunks to another. The
/// binding itself is shared, though: concurrent readers should use the
/// `addr`-taking methods rather than bind and then call these.
/// The cache is shared per file across all of its datasets. If the cache
/// currently holds state for a different dataset, all per-dataset state
/// (chunk index, chunk-index map, layout, and decompressed slots) is
/// dropped so the next access rebuilds it for this dataset. Reading the
/// same dataset again is a no-op, preserving the cache's benefit for
/// repeated/sequential access. Returns `true` if a reset occurred.
pub fn ensure_dataset(&self, addr: u64) -> bool {
let mut inner = self.lock();
let changed = inner.current != Some(addr);
inner.current = Some(addr);
changed
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
if inner.index_addr == Some(addr) {
return false;
}
inner.index = None;
inner.chunk_index = None;
inner.chunk_layout = None;
inner.slots.clear();
inner.slot_index.clear();
inner.current_bytes = 0;
inner.last_coord = None;
inner.index_addr = Some(addr);
true
}
/// Returns `true` if the bound dataset's chunk index has been built.
/// Returns `true` if the chunk index has been built.
pub fn has_index(&self) -> bool {
let inner = self.lock();
inner
.entry(inner.current())
.is_some_and(|e| e.index.is_some())
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.index
.is_some()
}
/// Build the bound dataset's chunk index from a pre-collected list of
/// `ChunkInfo`.
/// Build the chunk index from a pre-collected list of `ChunkInfo`.
///
/// The `rank` parameter is used to truncate offsets to spatial dims only
/// (B-tree v1 stores rank+1 offsets).
pub fn populate_index(&self, chunks: &[ChunkInfo], rank: usize) {
let addr = self.lock().current();
let _ = self.index_for::<core::convert::Infallible>(addr, rank, || Ok(chunks.to_vec()));
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
if inner.index.is_some() {
return; // already populated
}
let mut map = HashMap::with_capacity(chunks.len());
for ci in chunks {
let coord: ChunkCoord = ci.offsets.iter().take(rank).copied().collect();
map.insert(coord, ci.clone());
}
inner.index = Some(map);
}
/// Look up a chunk by its spatial coordinate in the bound dataset's index.
/// Look up a chunk by its spatial coordinate in the index.
pub fn lookup_index(&self, coord: &[u64]) -> Option<ChunkInfo> {
let inner = self.lock();
inner
.entry(inner.current())?
.index
.as_ref()?
.get(coord)
.cloned()
let inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.index.as_ref()?.get(coord).cloned()
}
/// Return all of the bound dataset's indexed chunks (order unspecified).
/// Return all indexed chunks as a `Vec<ChunkInfo>` (order unspecified).
pub fn all_indexed_chunks(&self) -> Option<Vec<ChunkInfo>> {
let inner = self.lock();
let index = inner.entry(inner.current())?.index.as_ref()?;
Some(index.values().cloned().collect())
let inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.index.as_ref().map(|m| m.values().cloned().collect())
}
/// Returns `true` if the bound dataset's `ChunkIndex` has been built.
// ----- Chunk index (pre-built coordinate → ChunkInfo map) -----
/// Returns `true` if the chunk B-tree index has been built.
pub fn has_chunk_index(&self) -> bool {
let inner = self.lock();
inner
.entry(inner.current())
.is_some_and(|e| e.chunk_index.is_some())
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.chunk_index
.is_some()
}
/// Build and store the bound dataset's `ChunkIndex`.
/// Build and store the chunk B-tree index from a pre-collected list of `ChunkInfo`.
pub fn populate_chunk_index(&self, chunks: &[ChunkInfo], rank: usize) {
let built = Arc::new(ChunkIndex::build(chunks, rank));
let mut inner = self.lock();
let addr = inner.current();
inner.touch(addr).chunk_index.get_or_insert(built);
inner.trim_datasets(addr);
}
/// Returns `true` if the bound dataset's chunk layout has been computed.
pub fn has_chunk_layout(&self) -> bool {
let inner = self.lock();
inner
.entry(inner.current())
.is_some_and(|e| e.chunk_layout.is_some())
}
/// Build and store the bound dataset's chunk layout (needs its
/// `ChunkIndex`; does nothing without one).
pub fn populate_chunk_layout(&self, ds_dims: &[usize], chunk_dims: &[usize], elem_size: usize) {
let mut inner = self.lock();
let addr = inner.current();
let entry = inner.touch(addr);
if entry.chunk_layout.is_some() {
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
if inner.chunk_index.is_some() {
return;
}
if let Some(idx) = entry.chunk_index.clone() {
entry.chunk_layout = Some(Arc::new(ChunkLayout::build(
&idx, ds_dims, chunk_dims, elem_size,
)));
inner.chunk_index = Some(ChunkIndex::build(chunks, rank));
}
// ----- Chunk layout (pre-computed assembly plan) -----
/// Returns `true` if the chunk layout has been computed.
pub fn has_chunk_layout(&self) -> bool {
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.chunk_layout
.is_some()
}
/// Build and store the pre-computed chunk layout for fast assembly.
pub fn populate_chunk_layout(&self, ds_dims: &[usize], chunk_dims: &[usize], elem_size: usize) {
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
if inner.chunk_layout.is_some() {
return;
}
if let Some(ref idx) = inner.chunk_index {
inner.chunk_layout = Some(ChunkLayout::build(idx, ds_dims, chunk_dims, elem_size));
}
}
/// Execute a function with a reference to the bound dataset's chunk
/// layout. Returns `None` if the layout hasn't been computed yet.
/// Execute a function with a reference to the chunk layout.
///
/// Returns `None` if the layout hasn't been computed yet.
pub fn with_chunk_layout<F, R>(&self, f: F) -> Option<R>
where
F: FnOnce(&ChunkLayout) -> R,
{
let layout = {
let inner = self.lock();
inner.entry(inner.current())?.chunk_layout.clone()?
};
Some(f(&layout))
let inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.chunk_layout.as_ref().map(f)
}
/// Try to get cached decompressed data for a chunk of the bound dataset.
// ----- Decompressed data cache (LRU) -----
/// Try to get cached decompressed data for a chunk coordinate.
///
/// Returns an owned copy; prefer [`Self::get_decompressed_aligned`] when
/// an `Arc`-shared buffer works for the caller.
/// O(1) lookup. Returns an owned copy for API compatibility with callers
/// that need a `Vec<u8>`; prefer [`Self::get_decompressed_aligned`] when
/// an `Arc`-shared buffer works for the caller, since that avoids the
/// copy entirely.
pub fn get_decompressed(&self, coord: &[u64]) -> Option<Vec<u8>> {
self.get_decompressed_aligned(coord)
.map(|arc| arc.as_slice().to_vec())
}
/// Reference-counted cached buffer for a chunk of the bound dataset.
/// Try to get a reference-counted clone of the aligned buffer for a chunk.
///
/// O(1) index lookup; the clone is an `Arc` refcount bump, not a copy of
/// the underlying decompressed data.
pub fn get_decompressed_aligned(&self, coord: &[u64]) -> Option<Arc<CacheAlignedBuffer>> {
let mut inner = self.lock();
let addr = inner.current();
inner.get_decompressed(addr, coord)
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.tick += 1;
let tick = inner.tick;
// Track sequential vs random access
let is_sequential = inner.last_coord.as_ref().is_some_and(|prev| {
// Sequential if exactly one dimension changed
let changes: usize = prev
.iter()
.zip(coord.iter())
.filter(|(a, b)| a != b)
.count();
changes <= 1
});
if is_sequential {
inner.stats.sequential_count += 1;
} else if inner.last_coord.is_some() {
inner.stats.random_count += 1;
}
inner.last_coord = Some(coord.to_vec());
let found = if let Some(&idx) = inner.slot_index.get(coord) {
inner.slots[idx].last_access = tick;
Some(Arc::clone(&inner.slots[idx].data))
} else {
None
};
if let Some(ref data) = found {
inner.stats.hits += 1;
inner.stats.bytes_read += data.len() as u64;
} else {
inner.stats.misses += 1;
}
found
}
/// Insert decompressed chunk data for the bound dataset into the LRU
/// cache, returning the `Arc`-shared buffer now cached.
/// Insert decompressed chunk data into the LRU cache.
///
/// The data is stored in a [`CacheAlignedBuffer`] so subsequent reads
/// return cache-line-aligned memory. Returns the `Arc`-shared buffer that
/// is now cached (or already was), so the caller can reuse it directly
/// instead of holding a separate copy of the same data.
pub fn put_decompressed(&self, coord: ChunkCoord, data: Vec<u8>) -> Arc<CacheAlignedBuffer> {
self.put_decompressed_aligned(coord, CacheAlignedBuffer::from_vec(data))
let aligned = CacheAlignedBuffer::from_vec(data);
self.put_decompressed_aligned(coord, aligned)
}
/// Insert an already-aligned buffer for the bound dataset.
/// Insert an already-aligned buffer into the LRU cache.
///
/// Returns the `Arc`-shared buffer now held by the cache (the one just
/// inserted, or the existing cached copy if `coord` was already present).
pub fn put_decompressed_aligned(
&self,
coord: ChunkCoord,
data: CacheAlignedBuffer,
) -> Arc<CacheAlignedBuffer> {
let data = Arc::new(data);
let mut inner = self.lock();
let addr = inner.current();
inner.put_decompressed((addr, coord), data)
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
let data_len = data.len();
// Don't cache if single chunk exceeds budget — still return the data
// to the caller, just don't retain it.
if data_len > inner.max_bytes {
return data;
}
/// [`Self::prefetch_hint_in`] for the bound dataset.
pub fn prefetch_hint(&self, next_coords: &[ChunkCoord]) {
let addr = self.lock().current();
self.prefetch_hint_in(addr, next_coords);
// Check if already present
inner.tick += 1;
let tick = inner.tick;
if let Some(&idx) = inner.slot_index.get(&coord) {
inner.slots[idx].last_access = tick;
return Arc::clone(&inner.slots[idx].data); // already cached
}
// ----- Whole-cache operations -----
// Evict until we have room
while inner.slots.len() >= inner.max_slots
|| (inner.current_bytes + data_len > inner.max_bytes && !inner.slots.is_empty())
{
// Find LRU slot
let lru_idx = inner
.slots
.iter()
.enumerate()
.min_by_key(|(_, s)| s.last_access)
.map(|(i, _)| i)
.unwrap();
let removed = inner.slots.swap_remove(lru_idx);
inner.slot_index.remove(&removed.coord);
// swap_remove moved the former last element into `lru_idx` (unless
// it *was* the last element) — fix up that element's index entry.
if lru_idx < inner.slots.len() {
let moved_coord = inner.slots[lru_idx].coord.clone();
inner.slot_index.insert(moved_coord, lru_idx);
}
inner.current_bytes -= removed.data.len();
inner.stats.evictions += 1;
}
/// Clear the entire cache (indexes + decompressed data + stats).
inner.current_bytes += data_len;
let new_idx = inner.slots.len();
inner.slot_index.insert(coord.clone(), new_idx);
inner.slots.push(CachedChunk {
coord,
data: Arc::clone(&data),
last_access: tick,
});
data
}
/// Clear the entire cache (index + decompressed data).
pub fn clear(&self) {
let mut inner = self.lock();
inner.datasets.clear();
inner.current = None;
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.index = None;
inner.index_addr = None;
inner.slots.clear();
inner.slot_index.clear();
inner.current_bytes = 0;
inner.tick = 0;
inner.last_coord = None;
inner.stats = AccessStats::default();
inner.chunk_index = None;
inner.chunk_layout = None;
}
/// Record that the given chunk coordinates are predicted to be accessed
/// soon (bookkeeping only).
///
/// This does **not** prefetch or pre-decompress anything — it only
/// checks whether each coordinate is already in the chunk index and
/// updates access-pattern stats accordingly. Real prefetching (e.g.
/// background pre-decompression) is not implemented.
pub fn prefetch_hint(&self, next_coords: &[ChunkCoord]) {
let inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
if inner.index.is_none() {
return;
}
drop(inner);
// For each predicted coordinate, verify it exists in the index.
// The index is already populated, so this is a no-op for known chunks.
// The purpose is to signal intent — callers can pre-decompress if needed.
// We touch the stats to record that prefetch hints were issued.
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
for coord in next_coords {
let exists = inner
.index
.as_ref()
.map(|idx| idx.contains_key(coord))
.unwrap_or(false);
if exists {
inner.stats.sequential_count += 1;
}
}
}
/// Return the current access pattern statistics.
pub fn access_stats(&self) -> AccessStats {
self.lock().stats.clone()
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.stats
.clone()
}
/// Update the sweep direction label in the access stats.
pub fn set_sweep_direction(&self, direction: &'static str) {
self.lock().stats.sweep_direction = Some(direction);
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.stats
.sweep_direction = Some(direction);
}
/// Number of decompressed chunks currently cached (all datasets).
/// Number of decompressed chunks currently cached.
pub fn cached_chunk_count(&self) -> usize {
self.lock().slots.len()
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.slots
.len()
}
/// Total bytes of decompressed data currently cached (all datasets).
/// Total bytes of decompressed data currently cached.
pub fn cached_bytes(&self) -> usize {
self.lock().current_bytes
}
/// Number of datasets whose chunk index is currently kept.
pub fn indexed_dataset_count(&self) -> usize {
self.lock().datasets.len()
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.current_bytes
}
}
@@ -967,92 +803,6 @@ mod tests {
assert_eq!(cache.cached_bytes(), 0);
}
#[test]
fn datasets_sharing_coordinates_stay_separate() {
let cache = ChunkCache::new();
let a = vec![make_chunk(vec![0, 0], 0x100, 8)];
let b = vec![make_chunk(vec![0, 0], 0x900, 8)];
let got_a = cache.chunks_for::<()>(1, 1, || Ok(a.clone())).unwrap();
let got_b = cache.chunks_for::<()>(2, 1, || Ok(b.clone())).unwrap();
assert_eq!(got_a[0].address, 0x100);
assert_eq!(got_b[0].address, 0x900);
// Built once per dataset: a second lookup doesn't call the builder.
let again = cache
.chunks_for::<()>(1, 1, || panic!("index rebuilt"))
.unwrap();
assert_eq!(again[0].address, 0x100);
cache.put_decompressed_in(1, vec![0], vec![1; 4]);
cache.put_decompressed_in(2, vec![0], vec![2; 4]);
assert_eq!(
cache.get_decompressed_in(1, &[0]).unwrap().as_slice(),
&[1; 4]
);
assert_eq!(
cache.get_decompressed_in(2, &[0]).unwrap().as_slice(),
&[2; 4]
);
assert!(cache.get_decompressed_in(3, &[0]).is_none());
assert_eq!(cache.cached_chunk_count(), 2);
// The bound-dataset methods see only the bound dataset.
cache.ensure_dataset(2);
assert_eq!(cache.lookup_index(&[0]).unwrap().address, 0x900);
assert_eq!(cache.get_decompressed(&[0]).unwrap(), vec![2; 4]);
}
#[test]
fn dataset_indexes_are_bounded() {
let cache = ChunkCache::new();
for addr in 0..(MAX_INDEXED_DATASETS as u64 + 10) {
cache
.chunks_for::<()>(addr, 1, || Ok(vec![make_chunk(vec![0], addr, 8)]))
.unwrap();
}
assert_eq!(cache.indexed_dataset_count(), MAX_INDEXED_DATASETS);
// One huge index evicts the others but is itself kept.
let huge: Vec<ChunkInfo> = (0..MAX_INDEXED_CHUNKS as u64)
.map(|i| make_chunk(vec![i], i, 8))
.collect();
let got = cache.chunks_for::<()>(9999, 1, || Ok(huge)).unwrap();
assert_eq!(got.len(), MAX_INDEXED_CHUNKS);
assert_eq!(cache.indexed_dataset_count(), 1);
}
#[test]
fn concurrent_readers_of_different_datasets_see_their_own_chunks() {
let cache = std::sync::Arc::new(ChunkCache::with_capacity(1 << 20, 64));
let handles: Vec<_> = (0..8u64)
.map(|t| {
let cache = std::sync::Arc::clone(&cache);
std::thread::spawn(move || {
for round in 0..500u64 {
let addr = (t + round) % 16;
let coord = vec![round % 4];
let chunks = cache
.chunks_for::<()>(addr, 1, || {
Ok((0..4).map(|c| make_chunk(vec![c], addr, 8)).collect())
})
.unwrap();
assert!(chunks.iter().all(|c| c.address == addr));
let want = vec![addr as u8; 8];
let got = match cache.get_decompressed_in(addr, &coord) {
Some(hit) => hit.to_vec(),
None => cache
.put_decompressed_in(addr, coord, want.clone())
.to_vec(),
};
assert_eq!(got, want);
}
})
})
.collect();
for h in handles {
h.join().unwrap();
}
}
#[test]
fn duplicate_insert_is_noop() {
let cache = ChunkCache::new();
crates/clawhdf5-format/src/chunk_grid.rs
-200
View File
@@ -1,200 +0,0 @@
//! Chunk-index linearisation shared by the Fixed Array and Extensible Array
//! chunk indexes (reader and writer).
//!
//! Both indexes store one element per chunk at a *linear* index, and the
//! library derives that index from the chunk's scaled coordinates
//! (`offset / chunk_dim`) using the dataset's **maximum** dimensions, not its
//! current ones (`H5D__farray_idx_get_addr` / `H5D__earray_idx_get_addr`,
//! via `layout->max_down_chunks`). A dataset whose current shape is smaller
//! than its maxshape therefore has gaps in the index, and laying it out by the
//! current shape puts every chunk after the first row in the wrong place.
//!
//! The Extensible Array adds one more step: its one unlimited dimension has no
//! finite chunk count, so the library *swizzles* the coordinates to make that
//! dimension the slowest-varying one (`H5VM_swizzle_coords`, which moves
//! `coords[unlim_dim]` to the front and shifts the dimensions before it right
//! by one) before linearising with `swizzled_max_down_chunks`. When the
//! unlimited dimension is already dimension 0 no swizzle happens.
#[cfg(not(feature = "std"))]
extern crate alloc;
#[cfg(not(feature = "std"))]
use alloc::{vec, vec::Vec};
use crate::error::FormatError;
/// How a chunk index maps linear element indexes to chunk coordinates.
#[derive(Debug, Clone)]
pub(crate) struct ChunkGrid {
/// Spatial chunk dimensions, in dataset order.
chunk_dims: Vec<u64>,
/// Chunks per dimension covering the *current* extent, in dataset order.
cur_chunks: Vec<u64>,
/// Dataset dimension stored at each linearisation position (slowest
/// first). The identity except for a swizzled Extensible Array.
order: Vec<usize>,
/// Linear stride of each linearisation position.
down: Vec<u64>,
}
impl ChunkGrid {
/// Grid for a Fixed Array index: row-major over the chunk counts of the
/// maximum dimensions (`max_dims`, falling back to the current dimensions
/// when the dataspace records none).
pub(crate) fn fixed_array(
cur_dims: &[u64],
max_dims: Option<&[u64]>,
chunk_dims: &[u64],
) -> Result<Self, FormatError> {
Self::build(cur_dims, max_dims, chunk_dims, None)
}
/// Grid for an Extensible Array index: like the Fixed Array, but the
/// unlimited dimension (the one whose maximum is `H5S_UNLIMITED`) is moved
/// to the slowest-varying position first.
pub(crate) fn extensible_array(
cur_dims: &[u64],
max_dims: Option<&[u64]>,
chunk_dims: &[u64],
) -> Result<Self, FormatError> {
let unlim = max_dims.and_then(|m| m.iter().position(|&d| d == u64::MAX));
Self::build(cur_dims, max_dims, chunk_dims, unlim)
}
fn build(
cur_dims: &[u64],
max_dims: Option<&[u64]>,
chunk_dims: &[u64],
unlim: Option<usize>,
) -> Result<Self, FormatError> {
let rank = chunk_dims.len();
if cur_dims.len() != rank || max_dims.is_some_and(|m| m.len() != rank) {
return Err(FormatError::ChunkedReadError(
"chunk index rank does not match the dataspace".into(),
));
}
if chunk_dims.contains(&0) {
return Err(FormatError::ChunkedReadError(
"chunk dimension is zero".into(),
));
}
let cur_chunks: Vec<u64> = cur_dims
.iter()
.zip(chunk_dims)
.map(|(&d, &c)| d.div_ceil(c))
.collect();
// Chunk counts of the maximum extent. An unlimited dimension has no
// finite count; it only ever sits in the slowest position, where its
// count never enters a stride. A (corrupt) maximum smaller than the
// current extent is widened so no allocated chunk becomes unreachable.
let max_chunks: Vec<u64> = (0..rank)
.map(|d| {
let max = max_dims.map_or(cur_dims[d], |m| m[d]);
if max == u64::MAX {
u64::MAX
} else {
max.div_ceil(chunk_dims[d]).max(cur_chunks[d])
}
})
.collect();
let mut order: Vec<usize> = (0..rank).collect();
if let Some(u) = unlim {
order.remove(u);
order.insert(0, u);
}
let mut down = vec![1u64; rank];
for p in (0..rank.saturating_sub(1)).rev() {
let next = max_chunks[order[p + 1]];
if next == u64::MAX {
// Only reachable with more than one unlimited dimension, which
// neither index type can describe.
return Err(FormatError::ChunkedReadError(
"array chunk index with more than one unlimited dimension".into(),
));
}
down[p] = down[p + 1].checked_mul(next).ok_or_else(|| {
FormatError::Overflow("chunk index linear stride overflows u64".into())
})?;
}
Ok(Self {
chunk_dims: chunk_dims.to_vec(),
cur_chunks,
order,
down,
})
}
/// Dataset-space offsets of the chunk stored at linear `index`, or `None`
/// when that chunk lies outside the current extent (the index still has a
/// slot for it; the library ignores such chunks on read).
pub(crate) fn offsets(&self, index: u64) -> Option<Vec<u64>> {
let rank = self.chunk_dims.len();
let mut offsets = vec![0u64; rank];
let mut rem = index;
for p in 0..rank {
let d = self.order[p];
let scaled = rem / self.down[p];
rem %= self.down[p];
if scaled >= self.cur_chunks[d] {
return None;
}
offsets[d] = scaled * self.chunk_dims[d];
}
Some(offsets)
}
/// Linear index of the chunk with scaled coordinates `scaled`
/// (`offset / chunk_dim` per dimension, in dataset order).
pub(crate) fn linear_index(&self, scaled: &[u64]) -> u64 {
self.order
.iter()
.zip(&self.down)
.map(|(&d, &stride)| scaled[d] * stride)
.sum()
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn fixed_array_uses_max_dims() {
// shape (4, 6), chunks (2, 3), maxshape (20, 10): 10 x 4 chunk grid.
let g = ChunkGrid::fixed_array(&[4, 6], Some(&[20, 10]), &[2, 3]).unwrap();
assert_eq!(g.offsets(0), Some(vec![0, 0]));
assert_eq!(g.offsets(1), Some(vec![0, 3]));
assert_eq!(g.offsets(2), None); // column chunk 2 is beyond the extent
assert_eq!(g.offsets(4), Some(vec![2, 0]));
assert_eq!(g.offsets(5), Some(vec![2, 3]));
assert_eq!(g.offsets(8), None); // row chunk 2 is beyond the extent
assert_eq!(g.linear_index(&[1, 1]), 5);
}
#[test]
fn extensible_array_swizzles_unlimited_dim() {
// maxshape (10, None): dim 1 is unlimited and becomes slowest.
let g = ChunkGrid::extensible_array(&[4, 6], Some(&[10, u64::MAX]), &[2, 3]).unwrap();
// max chunks of dim 0 = 5, so index = c1 * 5 + c0.
assert_eq!(g.linear_index(&[1, 0]), 1);
assert_eq!(g.linear_index(&[0, 1]), 5);
assert_eq!(g.offsets(5), Some(vec![0, 3]));
assert_eq!(g.offsets(6), Some(vec![2, 3]));
assert_eq!(g.offsets(2), None);
}
#[test]
fn extensible_array_unlimited_first_is_row_major() {
let g = ChunkGrid::extensible_array(&[4, 6], Some(&[u64::MAX, 30]), &[2, 3]).unwrap();
// max chunks of dim 1 = 10.
assert_eq!(g.linear_index(&[1, 1]), 11);
assert_eq!(g.offsets(11), Some(vec![2, 3]));
}
#[test]
fn rejects_two_unlimited_dims_after_the_first() {
assert!(ChunkGrid::fixed_array(&[4, 6], Some(&[u64::MAX, u64::MAX]), &[2, 3]).is_err());
}
}

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