Both indexes place each chunk at a linear index computed from the
dataset's maximum dimensions (libhdf5's max_down_chunks), and the
Extensible Array first swizzles its unlimited dimension to the slowest
position. We linearised by the current dimensions, so any dataset whose
shape was smaller than its maxshape, or whose unlimited dimension was not
the first, read back scrambled without an error: h5py libver="latest"
files with maxshape (10, None) or (20, 10), and the libhdf5 test files
h5fc_ext*.h5 and test_ld.h5.
The linearisation now lives in chunk_grid (shared with the writers), and
slots beyond the current extent are ignored as the library does.
read_fixed_array_chunks / read_extensible_array_chunks take the
dataspace's max dimensions.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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]>
read_raw_data_selection computed which chunks a selection intersects, threw
the answer away, decoded the entire dataset and picked elements out of it —
for contiguous layouts too. A 64x64 window of a 64 MB deflate dataset cost
105 ms, about half a full read; every selection cost the same whatever its
size.
New partial_read module: materialise only the selection's bounding box — the
overlapping rows of a contiguous dataset (straight from the file bytes) or the
overlapping chunks (only those are decompressed) — then run the existing
extractor over that buffer with the selection translated to the box origin, so
extraction semantics are exactly the full-read ones. It declines (falling back
to the old path) for All/None, compact/virtual/storage-less layouts, and boxes
covering more than half the dataset. That window now takes 0.39 ms, one row
2.7 ms, one column 5.2 ms.
Selections are validated against the dataset shape first. They were not: a
hyperslab past an edge came back padded with zeros and a point with an
out-of-range column wrapped into the next row, returning the wrong element
with no error. Now FormatError::SelectionOutOfBounds (also rank mismatch and
overlapping blocks); the facade's fill-aware path validates too.
Tests: equivalence against a reference extraction from a full read over 60
random hyperslabs/point lists per layout (contiguous, chunked, deflate) for
ranks 1-3. New read_harness bench binary with before/after in BENCHMARKS.md.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
HDF5 allocates lazily: a chunk nobody wrote doesn't exist in the file, and a
dataset nobody wrote has no data address. Such regions must read as the
dataset's fill value. There was no Fill Value message parser at all, so:
- a sparse chunked dataset read its holes as zeros — silently wrong whenever
the fill value isn't zero (h5py `fillvalue=-1` came back as 0);
- a dataset that was created but never written failed with NoDataAllocated /
"no address for chunked layout" where h5py returns a filled array.
New clawhdf5_format::fill_value: parses Fill Value messages v1-v3 and the old
0x0004 message (validated against HDF5 2.0 output under default and latest
libver), builds a fully filled dataset when there is no storage, and writes the
fill value into exactly the chunk-grid cells absent from the chunk index —
never mistaking a stored zero for a hole, clipping edge chunks, any rank. It is
skipped entirely for the default (zero) fill value. The chunk index dispatch is
extracted from read_chunked_data into a reusable list_chunks.
The reader, lazy and mmap facades apply it on full reads; selection reads go
through a fill-aware full read when the fill value matters. h5py interop test
compares against h5py's own readback, including a sparse 2-D dataset and a
hyperslab straddling allocated and unallocated chunks.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
