Files open in h5py again; float16 embedding storage #4

Merged
osobh merged 2 commits from feat/float16-embeddings into main 2026-09-25 03:18:07 +00:00
2 Commits
Author SHA1 Message Date
osobhandClaude Opus 5.5 d0db83812b feat(agent): MemoryConfig::float16 stores half-precision embeddings
CI / test-arm64 (pull_request) Successful in 1m19s
CI / test (pull_request) Successful in 4m58s
The setting was persisted in /meta and otherwise ignored: embeddings
were always written as f32. It now does what it says.

clawhdf5-format:
- `DatasetBuilder::with_f16_data` writes IEEE binary16 (numpy float16),
  rounding to nearest-even, and `make_f16_type`.
- `clawhdf5_format::float16` holds the f32 <-> f16 conversions, the one
  implementation the writer, the reader and the agent all use. Checked
  against the `half` crate on 16.7M f32 values and round-trips all 65536
  half values; the h5py interop tests confirm the rounding matches
  numpy's bit for bit (4020 values incl. ties, subnormals, overflow).
- Reading little-endian float16 as f32 has a fast path.

clawhdf5-agent:
- A float16 store writes /memory/embeddings as half precision, and
  `MemoryCache::half_precision` rounds each embedding as it enters the
  cache (save, update, WAL replay, and on load of a store still f32 on
  disk), so memory and file agree bit for bit and a store searches the
  same before and after a reopen (tested).
- Values beyond +-65504 are refused with the new
  `MemoryError::InvalidEntry` rather than stored as infinity, on every
  save path; batches are all or nothing, and a rejected ephemeral entry
  stays in the ephemeral tier. Breaking for exhaustive matches.
- CLI: `create --float16`. Off by default.

Measured on tank, 384-dim, six runs alternating order, medians
(search_harness --float16-study --full): at 100K the file goes from
154.0 to 80.8 MiB (-48%), checkpoint 752 -> 512 ms, open 300 -> 252 ms;
vector recall@10 against an exact scan and hybrid_search latency do not
change. At 10K open is 3 ms slower. Also a test that h5py opens a whole
agent store, f32 and float16, and decodes every dataset.

Docs: README, BENCHMARKS.md ("float16 embedding storage"), CHANGELOG
(including the h5py interop fixes in the previous commit), CLAUDE.md.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 12:00:38 -05:00
osobhandClaude Opus 5.5 5e4aa1c6bf fix(format): files we write now open in h5py and libhdf5
Two write-side bugs, both present in every release (the first at least
since v2.1.0), made libhdf5 refuse files written by clawhdf5. Our own
reader ignores both fields, and the interop suites only ever wrote f64
from our side, so nothing here caught them.

- Every f32 dataset: "sign bit position out of bounds". The float
  datatype encoder hard-coded the sign bit's position (bits 8-15 of the
  class bit field) to 63, which is right only for f64. It is now derived
  from the type: bit_offset + bit_precision - 1. This covered every
  agent store's embeddings, norms and activation weights.
- Every empty dataset: "invalid dataset size, likely file corruption".
  It was written with a real address and size 0, which trips libhdf5's
  `addr + size <= addr` overflow check. An empty contiguous dataset now
  gets the undefined address, as libhdf5 writes it. This covered every
  agent store without sessions or a knowledge graph.

Agent stores are rewritten in full at each checkpoint, so they become
readable at their next checkpoint on a fixed build; other files with f32
or empty datasets need rewriting. Both are recorded in
docs/known-issues.md.

Tests: the sign position byte for f32/f64, and h5py reading our f32
datasets (plain and chunked + deflate) bit for bit.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 12:00:26 -05:00