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osobhandClaude Opus 5.5 bb78d70b99 docs: changelog for the review follow-up fixes
CI / test-arm64 (pull_request) Successful in 1m7s
CI / test (pull_request) Successful in 5m31s
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:46:44 -05:00
osobhandClaude Opus 5.5 a7de15534c docs: conformance report after the read-gap fixes (569 of 697 ok)
Regenerated on tank at 10d1029: ok 467 -> 569, our-error 123 -> 14,
mismatch 15 -> 22 (six user-defined-link files moved from our-error to a
listing difference), no panics, hangs, crashes or OOM. Baseline raised.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:46:37 -05:00
osobhandClaude Opus 5.5 10d1029ead conformance: probe files with a user block and VDS the library's way
Superblock::parse now refuses a user-block offset, and the raw read path
no longer guesses a VDS fill value. The probe looks at the file from the
superblock on and reads virtual datasets with vds::read_virtual_dataset,
the dataset's fill value and its source-derived extent.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:46:08 -05:00
osobhandClaude Opus 5.5 883980f2bd test: compare h5py's v1 compound field names with what clawhdf5 reads
The test compared h5py against its own expected table, so it passed with
the fix reverted. Found by the adversarial review.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:44:05 -05:00
osobhandClaude Opus 5.5 d6e426e6d5 fix(agent): fail to open a store whose /meta has an unreadable attribute
Group::attrs now leaves out an attribute it cannot decode. The agent read
its settings through it, so a store whose float16 (or compression,
quantized_index, WAL mark, signature...) attribute could not be decoded
opened with the default in its place, and no error. /meta is now read
with attrs_with_errors and any unreadable attribute is a Schema error,
as it was before attrs became tolerant. Found by the adversarial review.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:43:53 -05:00
osobhandClaude Opus 5.5 256e7b89e4 fix(format): refuse variable-length and reference VDS data from another file
Their elements are global-heap IDs and object addresses in the source
file. The VDS reader copied them raw, so anything decoding them against
the virtual dataset's file got another object's data with no error.
Same-file sources are unaffected. Found by the adversarial review.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:42:57 -05:00
osobh f2e704abf3 Merge branch 'fix/p1-vds' into fix/p1-read-gaps
# Conflicts:
#	CHANGELOG.md
#	crates/clawhdf5-format/src/data_read.rs
2026-09-25 22:42:04 -05:00
osobh 61f36516d7 Merge branch 'fix/p1-attrs-links' into fix/p1-read-gaps
# Conflicts:
#	crates/clawhdf5-format/src/attribute.rs
#	crates/clawhdf5-format/src/group_v1.rs
#	crates/clawhdf5/src/lazy.rs
#	crates/clawhdf5/src/mmap_file.rs
#	docs/known-issues.md
2026-09-25 22:41:33 -05:00
osobh 45720fe5a6 Merge branch 'fix/p1-userblock-shared' into fix/p1-read-gaps
# Conflicts:
#	crates/clawhdf5-format/src/attribute.rs
#	crates/clawhdf5-format/src/datatype.rs
#	crates/clawhdf5-format/src/shared_message.rs
#	docs/known-issues.md
2026-09-25 22:40:55 -05:00
osobh adf961c883 Merge branch 'feat/p1-layout-v1v2' into fix/p1-read-gaps 2026-09-25 22:40:26 -05:00
osobhandClaude Opus 5.5 b4a44a2e66 feat(format): read unlimited and printf-style VDS mappings like libhdf5
Unlimited VDS mappings were refused, and printf-style source names
("f-%b.h5") were not expanded, so those regions read as fill (read-matrix
case 0470: 29 of 30 values wrong). All 7 virtual datasets in the libhdf5
test set use such mappings.

Implement H5Dvirtual.c's semantics in the vds module:
- %b is the block number, %% a literal %, other specifiers are an error;
  block j of the virtual selection comes from the source named with j,
  probing from 0 to the first missing source (printf gap 0);
- unlimited source/virtual selections are clipped to what the source's
  current extent fills (H5S_hyper_get_clip_extent_match, partial last
  block included);
- the extent is recomputed as H5Dget_space does (view "last available":
  the largest clip, never below what limited mappings need), exposed as
  vds::virtual_dataset_extent and used by Dataset::shape();
- a source in the other byte order is byte-swapped; other conversions stay
  an error.

Tests: vds_interop::vds_printf_source_names,
vds_unlimited_mappings_follow_source_extents (h5py low-level API, earliest
and latest format) and vds_libhdf5_test_files (vds-eiger, 4_vds and
vds-percival-unlim-maxmin from HDF5's tools/test/testfiles/vds, committed
as fixtures) all compare shape and values with h5py; unit tests for the
clip arithmetic, name parsing and mapping rules.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:10:09 -05:00
osobhandClaude Opus 5.5 17fa783dce fix(format): resolve SOHM-shared messages on every path
A message shared through the file's SOHM heap (H5Pset_shared_mesg_index)
is referenced by heap ID, which needs the SOHM table from the superblock
extension. Only message_data_with_sohm (used for fill values) loaded it;
resolve_shared_message passed no table, so a SOHM-shared datatype,
dataspace, filter pipeline or attribute failed with "invalid shared
message version: 2" and the dataset or attribute could not be read.
resolve_shared_message now loads the table when the reference carries a
heap ID.

Found while making attrs() tolerant: SOHM attributes turned from an
error into missing keys in the audit read matrix. With this fix all 36
SOHM cases there match h5py (datasets, fill values and attributes, every
shareable message type, libver earliest and latest).

Regression test: sohm_shared_messages_resolve (h5py writes files sharing
each message type on its own and all of them; values and attributes
checked).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:08:45 -05:00
osobhandClaude Opus 5.5 90e050944f fix(format): refuse a local heap whose free list leaves the heap
libhdf5 walks a local heap's free list when it loads the heap's data and
refuses the heap ("bad heap free list") when a free block starts or ends
outside the data segment, or links to offset 0. We never looked at the
free list, so a damaged old-style group listed names read from the broken
heap: once the user block of cve-2021-36977.h5 was applied, its root
listed eight garbage names where libhdf5 fails.

LocalHeap::validate_free_list (new) mirrors H5HL__fl_deserialize, with a
cycle bound, and accepts H5HL_FREE_NULL (1) or an all-ones head as the
end of the list. Like libhdf5 it runs when the first name is needed, not
on parse, so an empty group with a damaged heap still lists as empty
(cve-2018-13871.h5, cve-2024-29166.h5, gh-4431-poc-03.h5 keep matching
h5py).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:07:50 -05:00
osobhandClaude Opus 5.5 a6e90f3ee3 fix(format): apply the base address of files with a user block
A file may start with a user block (h5py userblock_size, h5jam), putting
the superblock at 512, 1024, ...; every address in the file is then
relative to the superblock. The signature search found it, but every
reader passed the whole file to the parsers, so addresses landed
userblock bytes early and the root group failed with
InvalidObjectHeaderVersion (twithub.h5, twithub513.h5,
h5clear_fsm_persist_user_*.h5).

Readers now view the file from the superblock on, taking the signature's
position as the base address as libhdf5 does: File (mmap, buffered,
from_bytes), MmapFile, LazyFile, AsyncHDF5File, the VOL and MPI VOL
readers, the HNSW loader and external VDS source files. File, MmapFile
and LazyFile gain user_block_size(). The new signature::split_user_block
returns the two parts, and Superblock::parse refuses a non-zero offset
(UserBlockNotStripped) so a format-level caller cannot silently apply
superblock-relative addresses to the whole file.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:07:50 -05:00
osobhandClaude Opus 5.5 0555794850 fix(format): read version-1 shared message addresses after the heap offset
A version-1 shared message (HDF5 1.6) embeds the target as a symbol-table
entry: after six reserved bytes comes a length-sized local-heap offset,
then the object header address. We read the heap offset as the address,
so datasets using a committed datatype in 1.6-era files (tcompound.h5,
tcompound2.h5) failed with InvalidObjectHeaderVersion. parse_shared_ref
now takes length_size and skips the offset, as libhdf5 does.

Resolving a reference also no longer falls back to the first message of
any type in the target header: a missing target message is
SharedMessageTargetMissing instead of garbage.

Fixture: tcompound.h5 from libhdf5's tools/test/testfiles (8 KiB).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:07:50 -05:00
osobhandClaude Opus 5.5 efc2dc53c9 fix(format): read array members of version-1 compound datatypes
HDF5 1.6 encoded a compound member that is a fixed-size array through
legacy per-member fields (dimensionality, permutation, four dimension
sizes) that the v1 decoder skipped, so a [4] i32 member read as one i32
with the wrong size. Build the array type from those fields as libhdf5
does (ignoring the permutation) and refuse more than four dimensions.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:07:50 -05:00
osobhandClaude Opus 5.5 5c2f656fe7 docs: first conformance report and baseline (42b81d9, tank)
CI / test-arm64 (pull_request) Successful in 1m6s
CI / test (pull_request) Successful in 5m0s
467 of 697 files read identically to h5py 3.16 / HDF5 2.0, 123 our-error,
15 mismatch (2 an h5py big-endian VL bug), 92 libhdf5 cannot read; no
panics, hangs, crashes or OOM.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:06:00 -05:00
osobhandClaude Opus 5.5 945b13a1f1 ci: nightly conformance sweep
Runs conformance/run.sh in rust:latest on a schedule and on demand, with its
own venv (pinned h5py/numpy/hdf5plugin/netCDF4) and hdf5-tools. Fails on any
panic, hang, crash or OOM in clawhdf5 and on a drop against
conformance/baseline.json; prints CONFORMANCE.md into the job log and
uploads nothing. Plain git checkout, no JavaScript actions.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:06:00 -05:00
osobhandClaude Opus 5.5 9179aa356e feat(conformance): in-repo, reproducible conformance sweep
conformance/run.sh fetches eight public HDF5 corpora pinned by commit
(conformance/corpus.txt) into a gitignored cache, reads every file with
clawhdf5 (conformance/probe, a crate outside the workspace) and with
h5py/libhdf5 (ref.py), and the CVE files with h5dump, each under a timeout
and an address-space limit; compare.py classifies the files, report.py
writes CONFORMANCE.md and check.py gates on panics/hangs/crashes/OOM and on
regressions against conformance/baseline.json. ~25 s once cached.

Changes from the ad-hoc audit harness:
- the probe compares non-IEEE-layout floats (N-Bit) and integers with a bit
  offset or reduced precision as the values libhdf5 converts them to, not
  raw file bytes: 8 files that showed as mismatches now read identically;
- ref.py exits without tearing down h5py objects: libhdf5 2.0 aborts while
  freeing them for two files about half the time, which flipped them
  between ok and h5py-cannot-read from run to run;
- the file list is defined (list_files.py): netCDF classic files are left
  out, 11 HDF5 files the ad-hoc sweep missed are in.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:06:00 -05:00
osobhandClaude Opus 5.5 e94a52a88b fix(format): read unmapped VDS elements as the virtual dataset's fill value
Elements of a virtual dataset that no mapping supplies (unmapped regions,
a missing source file, a missing source dataset) read as 0 instead of the
fill value libhdf5 returns — silent wrong data for any VDS created with a
non-zero fillvalue (read-matrix cases 0471/0472: -1 and 7 read as 0). A
missing source dataset was an error; libhdf5 reads it as fill.

Move VDS assembly into a new vds module following H5Dvirtual.c:
vds::read_virtual_dataset takes the dataset's fill value and a
VdsFileResolver that can refuse a name, and reports how many elements were
unmapped. Sources are read with their own fill value, and a source whose
datatype differs from the virtual dataset's is an error (libhdf5 converts).
File passes the dataset's fill value, resolves source names against the
virtual file's directory, and refuses names that leave it with an error
instead of reading them as fill. read_selection on a VDS goes through the
same fill-aware path.

The raw-read API (read_raw_data_full*) has no fill value, so it now errors
for a VDS with unmapped elements instead of guessing zeros.

Tests: vds_interop::vds_unmapped_regions_read_as_fill_value (external,
same-file, missing file/dataset, sparse source with its own fill, int
fill; earliest and latest format) and
vds_source_outside_directory_is_an_error_not_fill, both against h5py;
integration_test::v4_virtual_dataset_raw_api_refuses_to_guess_the_fill_value.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:03:47 -05:00
osobhandClaude Opus 5.5 d54a0f4737 feat(format): read the other attributes when one cannot be read
attrs() read every attribute of an object through extract_attributes_full,
so one attribute it could not read (a corrupt or unsupported attribute
message, or a heap object it could not locate) failed all of them — the
same shape as the huge-object bug, where one 8 KiB attribute hid every
attribute on a NetCDF file's root group.

- clawhdf5-format: new attribute::extract_attributes_tolerant returns the
  attributes it could read plus one error per attribute it could not.
  Errors in the attribute index itself (Attribute Info message, dense
  heap header, B-tree) still fail, since then it is unknown which
  attributes exist. extract_attributes_full is unchanged (strict); both
  share one implementation.
- clawhdf5: attrs() on Group/Dataset, MmapGroup/MmapDataset and
  LazyGroup/LazyDataset leaves an unreadable attribute out (documented),
  and the new attrs_with_errors() returns the map with the per-attribute
  errors. A value is either returned complete or not at all.

Regression test: one_unreadable_attribute_does_not_hide_the_others (h5py
writes 11 dense attributes; one message's version byte is corrupted;
before: attrs() failed with InvalidAttributeVersion(127), after: the 10
others come back with their values and one error is reported).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:03:09 -05:00
osobhandClaude Opus 5.5 aadfd18d4c fix(format): list soft links as their targets, like h5py
Group::datasets()/groups() (and the Mmap/Lazy handles) listed only hard
links, so a soft link to a dataset or group was missing, and dataset(name)
/ group(name) on a group handle could not open one. In old-style (symbol
table) groups a soft link's entry has no object header address, and the
listing failed outright trying to parse one.

The three facade handles each had their own copy of the child-listing
code; they now share group_v2::resolve_group_children, which returns hard
links plus soft links resolved to their targets (relative targets from
the group holding the link, via the new resolve_path_from). A dangling or
cyclic soft link, an external link and a user-defined link are left out —
h5py lists their names but cannot open them. Any other error met while
resolving is returned, not hidden.

Path resolution now walks a relative soft link's target from the group
holding it instead of rebuilding the path from the root (same result,
one less re-walk), and ignores "." components.

Regression test: soft_links_are_listed_as_their_targets (h5py writes
absolute, relative, group, dangling, cyclic and external links with
libver latest and earliest; listings compared with h5py for File,
MmapFile and LazyFile).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 22:00:46 -05:00
osobhandClaude Opus 5.5 2c6c6c176e fix(format): decode the version-1 VDS mapping list HDF5 2.0 writes
With a 2.0 low version bound, libhdf5 stores the VDS mapping list as heap
block version 1: every entry starts with a flags byte (0x04 same file, no
file name; 0x01/0x02 file/dataset name shared with an earlier entry, whose
index is stored in place of the name). The parser treated only a leading
0x04 byte as special, so a 0x00 flags byte read as an empty (same-file)
name and shared names were read as garbage.

Decode it as H5D__virtual_load_layout does, refusing unknown flags,
forward references and block versions above 1.

Test: vds_interop::vds_mapping_block_version1_shared_names (h5py
libver=("v200","v200") with repeated long names; failed before with
"unknown dataspace selection type") plus the exact heap block as a unit
test.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:59:15 -05:00
osobhandClaude Opus 5.5 190918a478 feat(format): decode hyperslab selection versions 1 and 2 in VDS mappings
libhdf5 serializes a VDS hyperslab as version 1 (irregular, 4-byte block
corners) for the default format bounds, and as version 2 (regular, 8-byte)
for unlimited selections in the 1.10 format. Only version 3 was accepted,
so every h5py VDS written with default libver failed with "only version-3
hyperslab selections are supported" (5 libhdf5 test files in the sweep).

Decode all three versions following H5S__hyper_deserialize, including
irregular hyperslabs (a union of blocks, enumerated in row-major order as
libhdf5 iterates them) and the all-ones "unlimited" count/block marker.
SerializedSelection exposes the raw form for unlimited-mapping support.

Test: vds_interop::vds_version1_irregular_hyperslab_selections compares
default-libver h5py VDS reads (contiguous, strided and 2-D block mappings)
with libhdf5's values.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:57:32 -05:00
osobhandClaude Opus 5.5 38d0d4de02 fix(format): skip user-defined links instead of failing the group
Link types 65-255 are user-defined: their target is only meaningful to
the application that registered the link class. LinkMessage::parse
rejects them with InvalidLinkType, and group traversal propagated that,
so one such link made the whole group unlistable and every path through
it unresolvable (libhdf5's tall.h5 and tudlink.h5, class 187).

Group traversal (compact and dense) now leaves user-defined links out,
the way h5py leaves out links it cannot open; reserved types (2-63) are
still an error.

Regression test: user_defined_links_do_not_break_the_listing, on
libhdf5's own tools/test/testfiles tall.h5 and tudlink.h5 (BSD-style
HDF5 licence, 10 KB and 1 KB), committed as fixtures.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:57:18 -05:00
osobhandClaude Opus 5.5 1c85986079 fix(format): read huge, tiny and filtered fractal heap objects
A heap ID's type is in bits 4-5 of its first byte (H5HF_ID_TYPE_MASK
0x30); bits 6-7 are the ID version. The reader took the type from bits
6-7, so every huge object ID (0x10) was decoded as a managed one and
failed — and since dense attributes are read all at once, one attribute
over the heap's 4 KiB managed limit made every attribute on its object
unreadable (netcdf4-python's issue671.nc / issue672.nc).

- Huge objects (type 1): located directly from the ID when address and
  length fit in it, otherwise through the huge-object v2 B-tree (record
  types 1 and 2); filtered huge objects are decoded with the heap's
  pipeline and their filter mask.
- Tiny objects (type 2): read from the ID itself.
- Filtered heaps: the header's pipeline is parsed (it was skipped short,
  so the header checksum was read from the wrong place), indirect-block
  entries for direct blocks carry their filtered size and mask, and
  direct blocks are decoded before objects are read from them.
- An unknown ID version is an error.

FractalHeapHeader gains huge_btree_address, filter_pipeline,
root_direct_block_filtered_size, root_direct_block_filter_mask,
offset_size and length_size; read_managed_object now accepts any ID type.

Regression tests (h5py-written, compared with h5py):
dense_attribute_stored_as_a_huge_heap_object, dense_group_with_a_huge_link,
dense_group_with_a_filtered_link_heap; unit tests
tiny_object_is_read_from_the_id, huge_object_with_a_direct_id,
unknown_heap_id_version_is_refused.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:55:51 -05:00
osobhandClaude Opus 5.5 c7092722aa fix(format): locate the address in version-1 shared messages
A version-1 shared message reference is version, type, six reserved bytes
and then an old-style symbol table entry: link-name offset (length size),
object header address, cache type, reserved, scratch. We read the address
straight after the reserved bytes, i.e. the link-name offset, and the
committed datatype lookup failed with InvalidObjectHeaderVersion (the bytes
checked in tcompound.h5: name offset 0x10, then 0x590 = /type1). Datasets
of 1.4/1.6-era files that use a committed datatype were unreadable.

Skip the name offset. parse_shared_ref has no length size, so add
parse_shared_ref_sized and use it in every internal caller;
parse_shared_ref keeps its signature and assumes length size == offset
size. The old parse_v1_ref unit test encoded the wrong layout and now uses
the real bytes.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:55:31 -05:00
osobhandClaude Opus 5.5 36356ba8a1 fix(format): keep the array dimensions of compound v1 members
Compound datatype version 1 carries, per member, a dimensionality and four
dimension sizes (HDF5 before 1.4 had no array class). The parser skipped
those 28 bytes, so a member such as `f: f32[4]` came back as a single f32
at the member's offset: the compound's size was right but its members were
wrong. libhdf5 wraps such a member in an array type of the first
`dimensionality` sizes and ignores the permutation; do the same, and
reject a dimensionality above 4 as libhdf5 does.

Only files old enough to also use layout message v1 have these, so this
became reachable with the previous commit (tarrold.h5, tcompound.h5).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:55:31 -05:00
osobhandClaude Opus 5.5 85eb7f5ce2 feat(format): read Data Layout message versions 1 and 2
HDF5 1.4/1.6-era files store the layout as version 1 or 2: version,
dimensionality, class, 5 reserved bytes, an address (contiguous and chunked
only), dimensionality 32-bit sizes (with the trailing element-size
dimension) and, for compact storage, a 32-bit size and the raw data. They
failed with InvalidLayoutVersion — 84 of the 686 files in the audit sweep,
205 datasets.

Map them onto the existing variants: chunked uses the same version-1
B-tree chunk index as version 3 and is reported as version 3, so every
chunked read path (filters, selections, caches) applies unchanged.
Contiguous size is the product of the stored dimensions, which is what
libhdf5 computes from the dataspace; a disagreement fails the reader's size
check instead of returning wrong data.

Fixtures are HDF5's own deflate.h5 (v1, chunked + deflate) and
h5ex_g_iterate.h5 (v2, contiguous, one unallocated dataset); the new
interop test compares every dataset byte for byte against h5py.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:55:31 -05:00
osobhandClaude Opus 5.5 8196fab72a fix(format): read v2 B-tree internal nodes with libhdf5's pointer widths
An internal node's child pointer is an address, the child's record count
and (below the first internal level) the child subtree's total record
count. libhdf5 (H5B2__hdr_init) encodes the record count in the width of
a leaf's maximum and the subtree total in the width of cum_max_nrec for
that depth, computed level by level from the node size. The reader
guessed 2 * leaf_max and leaf_max^depth, which agree at depth 2 but not
at depth 3: a 24 000-link group's name index has depth 3, its root's
pointers were read 3 bytes wide instead of 2, and listing failed with a
garbage heap offset.

Regression tests: dense_group_with_a_three_level_name_index (h5py writes
24 000 links; listing compared with h5py) and
subtree_capacity_matches_libhdf5.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:53:00 -05:00
osobhandClaude Opus 5.5 8ebd488d9e fix(format): size fractal heap child indirect blocks by their row's span
A child indirect block in row r of a fractal heap's doubling table spans
that row's block size of heap space, so it has
log2(size) - log2(start_block_size * width) + 1 rows (libhdf5's
H5HF__dtable_size_to_rows). The reader used row - first_indirect_row + 1,
which undercounts, so every object stored past the root block's direct
rows (512 KiB with libhdf5's defaults) was unreachable: dense groups with
a few thousand long link names, or ~20 000 short ones, could not be listed.

Regression test: dense_group_whose_heap_outgrows_the_root_direct_rows
(h5py writes 2 500 links with 248-byte names; listing compared with h5py).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:52:07 -05:00
osobh 42b81d9f1c Merge pull request 'Fix silent wrong data and libhdf5 interop found by the HDF5 audit' (#11) from fix/phase0-correctness into main
CI / test-arm64 (push) Successful in 1m7s
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Reviewed-on: #11
2026-09-26 02:42:53 +00:00
osobhandClaude Opus 5.5 72b9cfb1e1 docs: record the 2026-09-25 HDF5 audit fixes and open gaps
CI / test-arm64 (pull_request) Successful in 1m21s
CI / test (pull_request) Successful in 5m59s
CHANGELOG: upgrade notes (changed read results for max-shape files,
saturating conversions, new writer errors, format-crate API changes) and
the reader/writer correctness fixes. known-issues: the silent-wrong-data
table with before/after sweep numbers, the gaps still open, and a
correction to the Extensible Array entry, which said files we wrote were
unaffected. CLAUDE.md: clawhdf5-gpu is vector distance computation, not
I/O, and clawhdf5-filters holds only deflate backends (no Blosc).

Also a facade test that libhdf5's 20-bit N-Bit float test data reads as
libhdf5's values.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:26:56 -05:00
osobhandClaude Opus 5.5 650f355219 ci: run the hdf5plugin LZ4/Zstd interop tests
The interop step built writer_h5py_tests without the lz4/zstd features,
so the hdf5plugin round-trips added with the registered LZ4 framing and
the Zstd content-size fix never compiled in CI, and CI never installed
hdf5plugin.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:24:50 -05:00
osobh 7f5cfee281 Merge branch 'fix/p0-filters' into fix/phase0-correctness
# Conflicts:
#	crates/clawhdf5-format/src/filters.rs
2026-09-25 21:21:19 -05:00
osobh c5302e587e Merge branch 'fix/p0-writer-meta' into fix/phase0-correctness 2026-09-25 21:20:59 -05:00
osobh e1115bc92a Merge branch 'fix/p0-reader-numeric' into fix/phase0-correctness 2026-09-25 21:20:59 -05:00
osobh 36d7a6f234 Merge branch 'fix/p0-chunked-read' into fix/phase0-correctness 2026-09-25 21:20:59 -05:00
osobh 4b23ad697c Merge branch 'fix/p0-chunk-index' into fix/phase0-correctness 2026-09-25 21:20:59 -05:00
osobhandClaude Opus 5.5 e7f2d8575d fix(format): import format! for the no_std chunk index planner
The maxshape checks added to chunked_write use format!, which a no_std
build has to import from alloc (scripts/check-nostd.sh).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:18:13 -05:00
osobhandClaude Opus 5.5 7c1968a34a fix(format): resolve shared fill value messages instead of zero-filling
dataset_fill_value treated a shared Fill Value message as "no fill
value", so unwritten storage of a dataset whose fill value lives in the
file's shared-message (SOHM) heap read as zeros rather than its fill
value. libhdf5 shares fill values whenever the file has a SOHM index for
them.

- fill_value::dataset_fill_value_in follows the reference (another object
  header, or the SOHM heap); read_full_with_fill and the facade's
  selection read use it.
- dataset_fill_value, which has no file to follow a reference into, now
  returns UnresolvedSharedMessage for a shared message instead of None.
- shared_message::load_sohm_table / message_data_with_sohm load the SOHM
  table from the superblock extension on demand.
- parse_sohm_table skipped each index's leading version byte, reading
  every field one byte off; SOHM references could never resolve.

Fixture shared_fill_value.h5 (HDF5 2.0, gen_shared_fill.py): sohm_b read
[0,1,2,3,0,0,0,0] and now reads [0,1,2,3,-7,-7,-7,-7], as h5py does.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:18:12 -05:00
osobhandClaude Opus 5.5 6db13c60b8 docs: changelog for the filter interop fixes
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:17:08 -05:00
osobhandClaude Opus 5.5 d99426be94 fix(format): allow Fletcher32 ahead of a compressor in the pipeline
libhdf5 applies filters in pipeline order, so with Fletcher32 before
deflate (h5repack_filters.h5 /dset_all: shuffle, fletcher32, deflate; or
h5py's set_fletcher32() then set_deflate()) the compressor holds the
chunk plus a 4-byte checksum. decompress_chunk bounded every stage by the
chunk size and rejected it: "deflate: output exceeds size limit". Bound
each stage by the chunk size plus 4 bytes per Fletcher32 that precedes
it in the pipeline.

Test: fletcher32_before_deflate_decodes (h5py-written chunk, and our own
shuffle + fletcher32 + deflate round trip); failed before.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:16:48 -05:00
osobhandClaude Opus 5.5 f5505fb03d fix(format): keep maxshape == shape datasets contiguous
Any maxshape forced chunked storage, even one equal to the shape, which
cannot grow. h5py and the library store such a dataset contiguously; we
now do too unless chunks (or a filter) are requested.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:15:55 -05:00
osobhandClaude Opus 5.5 95dcb04454 fix(format): scale-offset float decode with libhdf5's arithmetic
D-scale floats were rebuilt as `minval + code / 10^D` in f64 and then
rounded to f32 once, but libhdf5 (H5Z_scaleoffset_modify_3/4 with
`float`/`powf`) computes `(float)(int)code / powf(10, D) + min` in single
precision. The two differ by 1 ULP for some values: le_data.h5
/Scale_offset_float_data_{le,be} gave 1.6663332 (0x3fd54a69) where
libhdf5 gives 1.6663333 (0x3fd54a6a). Use f32 arithmetic for 4-byte
floats and `(double)(long)code / pow(10, D) + min` for 8-byte ones.

Test: scaleoffset_float_dscale_matches_libhdf5_bits (le_data.h5 float
LE/BE and double chunks, bit-exact against h5py); failed before.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:15:49 -05:00
osobhandClaude Opus 5.5 1dba7b465a fix(format): index datasets with several unlimited dims by B-tree v2
A dataset with more than one unlimited dimension got an Extensible Array
index, which libhdf5 refuses ("already found unlimited dimension"), so
the whole file failed to open in h5py and h5dump. The previous commit
turned that into a write error; this one writes what the library itself
uses there: a version-2 B-tree chunk index (record type 10/11), as a
single leaf of the library's 2048-byte node size, or a larger leaf when
the records do not fit. The root's record count is 16-bit, so more than
65535 chunks is still refused rather than written wrong.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:14:47 -05:00
osobhandClaude Opus 5.5 57e938c438 fix(format): honour unknown-message flags the way libhdf5 does
The object header parser failed on an unknown message with flag bit 3
set and ignored bit 7. Per the spec, bit 3 means "fail if unknown and
the file is opened for writing" and bit 7 "fail if unknown, always".
The parser only reads, so it now ignores bit 3 (as libhdf5 does for a
read-only open) and refuses bit 7, in v1 headers, v2 headers and their
continuation chunks.

On libhdf5's conformance file tbogus.h5 (added as a fixture) we used to
refuse Dataset2 and open Dataset3; we now match libhdf5: Dataset1, 2, 4
and 5 open, Dataset3 is refused.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:14:30 -05:00
osobhandClaude Opus 5.5 3000b40cf3 fix(format): N-Bit pass-through flag and no-op (enum) members
- libhdf5 sets cd_values[1] ("need not compress") when every field is
  already full width and then stores the chunk unchanged
  (H5Z__filter_nbit: `if (cd_values[1]) HGOTO_DONE`). We ignored it and
  tried to unpack, so tfilters.h5 / h5stat_filters.h5 `/all` (shuffle +
  szip + deflate + fletcher32 + N-Bit) failed with "nbit: packed data too
  short". A type with no N-Bit parameters (cd = [3, 1, nelmts]) is now
  accepted the same way.
- Class 4 (H5Z_NBIT_NOOPTYPE: enum, string, opaque, ... members) is
  stored whole, 8 bits per byte; it was UnsupportedFilter(5)
  (h5repack_nested_8bit_enum_deflated.h5).

N-Bit on floats was not wrong in the filter: for le_data.h5 /
Nbit_float_data_* our output equals libhdf5's decoded bytes in the file
datatype (a 20-bit float, offset 7, bias 31). h5py's values differ
because libhdf5 then converts that custom float layout to IEEE, which
our datatype reader does not do; nbit_float_matches_libhdf5_file_type_bytes
pins the filter output and the doc comment says where conversion belongs.

Tests: nbit_need_not_compress_is_passthrough,
nbit_in_multi_filter_pipeline_matches_libhdf5 (tfilters.h5 chunk, szip
feature), nbit_compound_with_enum_member_matches_libhdf5 all failed
before; nbit_float_matches_libhdf5_file_type_bytes (guard).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:14:14 -05:00
osobhandClaude Opus 5.5 bc820fbd8c fix(format): refuse path-like group and dataset names
FileWriter writes the root group plus one level of groups; it has no way
to create intermediate groups. create_group("a/b") therefore stored a
single link literally named "a/b", which no HDF5 reader can resolve
(h5py: "component not found"). Nesting would mean restructuring the
writer's layout around a group tree, so for now finish() rejects any
group, dataset or external-link name that is empty, "." or contains '/'.
Attribute names may still contain '/'.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:13:08 -05:00
osobhandClaude Opus 5.5 9066d34eaa fix(format): key the shared chunk cache by dataset
A File is Send + Sync and keeps one ChunkCache for all its datasets.
The cached readers bound that cache to "the current dataset" with
ensure_dataset(addr), then checked, built and read its index and its
decompressed chunks in separate lock acquisitions. Two threads reading
two chunked datasets interleaved those steps, so one could store its
chunk index under the other's binding, or get the other's decompressed
chunk for the same coordinate: wrong data, or an index-out-of-bounds
panic when the ranks differed (16 threads x 40 reads over 24 datasets
panicked on every run).

The cache now keeps per-dataset state keyed by chunk-index address:
the chunk index, ChunkIndex and ChunkLayout per dataset (held as Arcs,
built outside the lock, first writer wins), and decompressed chunks
keyed by (address, coordinate). The chunked readers use the new
addr-taking methods (chunks_for, chunk_layout_for, get/put_decompressed_in,
prefetch_hint_in) exclusively. Memory stays bounded: decompressed data by
the existing byte/slot budget across datasets, indexes by at most 64
datasets and 2^20 index entries in total, dropping the least recently
used dataset's index first. Switching datasets no longer throws away the
other datasets' cached chunks.

The address-less methods remain and act on the dataset last bound with
ensure_dataset; they are documented as not for concurrent readers.

Regression: threads_reading_different_datasets_get_their_own_chunks
(crates/clawhdf5/tests/concurrent_chunk_cache.rs), plus cache unit tests
datasets_sharing_coordinates_stay_separate, dataset_indexes_are_bounded
and concurrent_readers_of_different_datasets_see_their_own_chunks.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:12:52 -05:00
osobhandClaude Opus 5.5 540fa08907 fix(format): write chunk indexes over the max extent, swizzled for EA
The writer indexed chunks by their position in the current shape, the
same mistake the reader had. With a finite maxshape larger than the shape
the Fixed Array was sized for the shape, so libhdf5 looked up chunks past
its end ("addr overflow"); with the unlimited dimension anywhere but first,
e.g. maxshape (20, None), libhdf5 swizzles that dimension to the slowest
position and read our Extensible Array scrambled. Two unlimited dimensions
produced a file libhdf5 refused to open ("already found unlimited
dimension").

Chunks are now placed with the shared chunk_grid linearisation: Fixed
Array slots cover every chunk of the maximum extent (unwritten ones
undefined), Extensible Array indexes are swizzled, Single Chunk is only
used when the maximum extent is one chunk, and a maxshape that is smaller
than the shape, has more than one unlimited dimension, or would need an
absurd Fixed Array is an error instead of a bad file.
build_chunked_data_from_precompressed now returns a Result.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:12:47 -05:00
osobhandClaude Opus 5.5 14876b8ae5 fix(format): give empty string attributes a 1-byte type
An empty AttrValue::String (or a StringArray of empty strings) was
written with a size-0 fixed-length string type. libhdf5 rejects that
("invalid datatype size"), and the failure takes every attribute on the
object with it. Strings are now at least 1 byte, NUL-padded, which is
how h5py stores "" and reads back as "" in both h5py and our reader.
check_encodable also refuses a size-0 string type passed in directly.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:12:12 -05:00
osobhandClaude Opus 5.5 5935e13866 fix(format): decode SZIP chunks the way libhdf5 does
SZIP-filtered datasets from libhdf5 came back as garbage or zeros with no
error (ref_szip.h5, h5repack_szip.h5, noencoder.h5, le_data/be_data
Szip_float_data_*), and 64-bit ones failed with "invalid bits per
sample" (h5wasm compressed.h5). The decoder called aec_buffer_decode
directly, but libhdf5 goes through szlib's SZ_BufftoBuffDecompress
(H5Zszip.c), which libaec implements with reshaping (sz_compat.c).
Differences, all fixed:

- H5Zszip.c prefixes the stream with the 4-byte LE uncompressed size; it
  was fed to libaec as data.
- 32- and 64-bit samples are coded as byte planes of 8-bit samples and
  must be de-interleaved.
- The reference sample interval is ceil(pixels_per_scanline /
  pixels_per_block), not a fixed 128.
- Scanlines that are not a whole number of blocks are padded and must be
  unpadded.
- Byte order comes from the MSB option bit; LE data was decoded as MSB.

Test: szip_decodes_libhdf5_chunks_exactly compares chunks from HDF Group
test files (noencoder.h5, le_data.h5) and an h5py-written file (64-bit,
16-bit, padded scanlines, NN and EC) byte for byte with h5py's values;
it failed before on the first case.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:11:24 -05:00
osobhandClaude Opus 5.5 8c3ef996ea fix(format): write fill times with libhdf5's codes; add fill values
FillTime::to_byte had the fill-time field rotated against libhdf5
(H5D_FILL_TIME_ALLOC = 0, NEVER = 1, IFSET = 2): Never was written as
ALLOC, Alloc as IFSET and IfSet as NEVER, as h5py reported. The flags
byte is now late allocation plus the right code, and FillTime::from_byte
decodes it.

The default becomes IfSet, which is libhdf5's default and exactly the
byte (0x0a) every dataset was already written with, so default output
does not change; `Alloc` was documented as the C library's default but
never was. DatasetCreateProps follows.

DatasetBuilder::with_fill_value sets a user-defined fill value (one
element's stored bytes, checked against the datatype size), written as a
defined value in the fill value message. h5py reports it, and extending
the dataset in h5py fills the new elements with it.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:11:07 -05:00
osobhandClaude Opus 5.5 74fdf0582b fix(format): write paged files libhdf5 can open
FileWriter::with_page_size wrote a "version 4" superblock with an extra
page-size field. HDF5 has no superblock version 4, so libhdf5 refused
every such file ("bad superblock version number").

A paged file is now what libhdf5 itself writes for fs_strategy="page":
a v3 superblock whose extension object header holds a File Space Info
message (strategy PAGE, the page size, free space not persisted; same
bytes and flags as HDF5 2.0), with the file padded to a whole page.
h5py opens it, reports the strategy and page size, and can modify it in
r+ mode. Page sizes outside libhdf5's 512 B..1 GiB are an error.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:09:31 -05:00
osobhandClaude Opus 5.5 44f5f8b5c5 fix(format): index every Extensible Array chunk, not just the first 244
The Extensible Array writer only filled the index block's 4 inline
elements and the 6 data blocks it addresses directly (240 elements); its
super block addresses were always undefined. Chunks from index 244 on were
written to the file but never indexed, so they read back as fill values in
our reader and in libhdf5, without an error.

The writer now lays out data blocks and super blocks for any element
count as H5EA__hdr_init sizes them, pages data blocks larger than 1024
elements (page-init bits in the owning super block), leaves blocks with no
defined element unallocated, and records real header statistics
(max_idx_set is one past the highest defined index).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:09:29 -05:00
osobhandClaude Opus 5.5 2f252df084 fix(format): return whole VL sequences from read_vl_bytes
read_vl_bytes cut each element to the reference's length field, which
counts sequence elements, not bytes: a VL int32 [1, 2, 3] came back as
3 bytes. Return the whole global-heap object, which is element count x
base size bytes. No in-tree caller depended on the old behaviour.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:08:37 -05:00
osobhandClaude Opus 5.5 c8c2930fc0 fix(format): read enum and bool datasets through their base integer type
read_i64/read_u64/read_i32/read_f64/read_f32 refused enumeration
datatypes, including h5py's bool (an enum of int8), with a type
mismatch. Read them as their base type's integer values, the way array
datatypes already read through theirs.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:08:37 -05:00
osobhandClaude Opus 5.5 417c9516ca fix(format): decode floats by their datatype fields, not their size
Every 2-byte float was decoded as IEEE half, so bfloat16 (HDF5 2.0's
H5T_FLOAT_BFLOAT16*, or any custom 8-bit-exponent type) read wrong:
1.5 as 1.9375, +inf as NaN. 1-byte FP8 floats were refused.

Read the exponent/mantissa location and size and the bias from the
datatype message: IEEE half/single/double keep their existing paths
(half still through clawhdf5_format::float16), any other IEEE-style
layout up to 64 bits whose values fit f64 (bfloat16, FP8 E4M3/E5M2, ...)
is decoded generically, and the bulk-copy and zero-copy fast paths now
require the IEEE layout rather than just the size. Datatypes with fields
that describe no float still fall back to IEEE by size; layouts that
cannot be represented in f64 (x87 80-bit, binary128) remain an error.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:08:37 -05:00
osobhandClaude Opus 5.5 53dbddb07b fix(format): saturate out-of-range integer reads instead of truncating
Reading wider or differently-signed integers kept the low bits: i64
2^40+5 read as i32 was 5, u64::MAX read as i64 was -1, and -1 read as
u64 was 4294967295. u32 data read as i32 also took the bulk-copy fast
path meant for i32. Saturate at the target range like libhdf5's hard
conversions (a negative value read as unsigned is 0), and keep the i32
fast path to signed data.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:08:37 -05:00
osobhandClaude Opus 5.5 081341b433 fix(format): convert float data read as integers instead of returning bit patterns
read_i32/read_i64/read_u64 on a floating-point dataset reinterpreted the
IEEE bits (1.5 read as i64 was 4609434218613702656). Convert like
libhdf5's hard conversions instead: truncate toward zero and saturate at
the target range; NaN reads as 0.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:08:37 -05:00
osobhandClaude Opus 5.5 d074385944 fix(format): read partial edge chunks stored unfiltered
Layout message v4 flag bit 0 (H5D_CHUNK_DONT_FILTER_PARTIAL_CHUNKS, set
with H5Pset_chunk_opts) makes libhdf5 store every chunk that extends past
the dataset's extent without the filter pipeline, while its filter mask
still reads 0. The parser ignored the flag, so readers tried to inflate
raw bytes: libhdf5's own h5fc_edge_v3.h5 failed with "deflate: ...
unknown compression method".

DataLayout::Chunked gains dont_filter_partial_edge_chunks (always false
for v3), and list_chunks — the one place every read path gets its chunk
list from — marks such partial chunks as having skipped every filter, so
the full, cached, indexed, parallel and selection readers all copy them
as-is. chunked_write.rs gets `..` in one exhaustive test pattern for the
new field.

Regression: libhdf5_edge_chunk_fixture_reads (h5fc_edge_v3.h5 from the
HDF5 tools test files, committed as a 2.5 KB fixture), and
h5py_unfiltered_partial_edge_chunks_read (the flag set through h5py's
bundled libhdf5 via ctypes, as h5py has no binding for it: fixed array,
extensible array and B-tree v2 indexes, 1-D and 2-D, plus a hyperslab
of the last chunk), and v4_chunked_dont_filter_partial_edge_chunks_flag.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:08:36 -05:00
osobhandClaude Opus 5.5 4a1876faf2 fix(format): page Fixed Array data blocks past 1024 chunks
The Fixed Array writer always packed every element into one data block
behind one checksum. Past 2^10 elements libhdf5 (and our reader) expect a
paged block: a page-init bitmap after the prefix, then one checksummed page
per 1024 elements. Any dataset with more than 1024 chunks and no unlimited
dimension failed with "incorrect metadata checksum" in h5py, h5dump and
our own reader.

build_fixed_array_at now takes one Option<WrittenChunk> per array slot so
later fixes can leave unallocated slots.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:07:08 -05:00
osobhandClaude Opus 5.5 be88e3fec7 fix(format): encode Time, BitField, Opaque and Reference datatypes
Datatype::serialize returned an empty message for these four classes, so
any dataset or attribute of them (including a Raw attribute copied from
another file) was unreadable by libhdf5 ("ran off end of input buffer
while decoding"). They now encode exactly as libhdf5 does: legacy object
and region references as datatype version 1, H5T_STD_REF kinds as version
4 with their encoding version, opaque tags NUL-padded to 8 bytes.

Parsing an opaque tag now stops at its first NUL, so libhdf5's padding
no longer becomes part of the tag. Datatype::check_encodable rejects
what has no encoding (an opaque tag over 248 bytes); FileWriter::finish
calls it for every dataset and attribute type.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:06:51 -05:00
osobhandClaude Opus 5.5 e162c013fd fix(format): bound each filter stage by what the stages before it produce
Every decode stage was capped at the chunk's decoded size. That holds
only when every filter ahead of the codec preserves size; Fletcher32
does not (it appends a 4-byte checksum), so a pipeline with Fletcher32
before deflate (NetCDF-4's fletcher32 -> shuffle -> deflate ordering,
h5repack's "all filters") failed with "deflate: output exceeds size
limit" on every chunk.

decompress_chunk_masked now computes each stage's bound by running the
chunk size forward through the filters that precede it in write order
(and that the chunk's mask did not skip): shuffle keeps the size,
Fletcher32 adds 4, any codec adds at most n/8 + 64. The cap is still a
small constant factor of the chunk, so a decompression bomb is rejected
as before (tested).

Shuffle also had to learn libhdf5's handling of a length that is not a
whole number of elements (chunk + checksum): shuffle the whole elements
and leave the trailing bytes in place, in both directions. It used to
refuse such data.

Regression: h5py_fletcher32_before_deflate_reads (fletcher->shuffle->
gzip, fletcher->gzip, shuffle->fletcher->gzip, and a 2-D i32 grid),
fletcher32_ahead_of_deflate_stays_bounded and
shuffle_leaves_a_partial_trailing_element_in_place.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:06:22 -05:00
osobhandClaude Opus 5.5 06dda26d85 fix(format): stop writing pcodec under Granular BitRound's filter ID
Pcodec chunks were written as filter 32023, which the HDF Group registry
assigns to Granular BitRound (GBR). Pcodec has no registered ID (checked
2026-09-25 against hdf5_plugins/docs/RegisteredFilterPlugins.md, which
ends at 32033 with no pcodec entry). GBR's decode is a pass-through, so
libhdf5 with that plugin loaded would have returned the compressed bytes
as the dataset's values.

Write pcodec as 480, from the registry's testing/private range (256-511),
named "pcodec (clawhdf5 private)", and document it as non-interoperable:
only clawhdf5 with the `pcodec` feature reads it. Chunks under 32023 are
still read as pcodec when the filter is named exactly "pcodec" (what
clawhdf5 <= 2.7.0 wrote); any other 32023 is UnsupportedFilter.

Test: pcodec_uses_private_id_and_reads_legacy_32023.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:06:07 -05:00
osobhandClaude Opus 5.5 585e14d5e2 fix(format): honour each bit of a chunk's filter mask
A chunk's filter mask has one bit per pipeline filter; bit i set means
filter i was not applied to that chunk (an optional filter that
declined, or a direct chunk write). Every read path treated any nonzero
mask as "no filters applied" and returned the stored bytes, so a chunk
that skipped only gzip in a shuffle+gzip pipeline came back still
shuffled (h5py write_direct_chunk with filter_mask=0b10: 8 of 32 values
wrong).

decompress_chunk_masked undoes the filters the mask leaves set and skips
the rest; an unsupported filter is no longer an error when the chunk
skipped it. The full, cached, sweep, indexed, parallel and selection
(partial_read) paths all use it, and a chunk is copied straight from the
file only when every filter was skipped. decompress_chunk is the mask-0
case.

Regression: h5py_partial_filter_mask_skips_only_masked_filters (1-D
shuffle+gzip with masks 0, 0b10 and 0b11; 2-D with 0b01; full and
hyperslab reads) and filter_mask_skips_only_the_masked_filters.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:05:07 -05:00
osobhandClaude Opus 5.5 183d96ee26 fix(format): record the content size in zstd frames
Filter 32015 chunks were written with the streaming encoder
(zstd::encode_all), whose frames carry no content size. The registered
HDF5 Zstandard filter (H5Zzstd.c, libhdf5 + hdf5plugin) sizes its output
from ZSTD_getFrameContentSize and fails on such frames, so h5py could not
read our zstd datasets ("filter returned failure during read"). Compress
with the one-shot API, which records the size.

Tests: zstd_frames_record_content_size (content size was None before),
hdf5plugin_reads_our_zstd (ignored interop test; failed before).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:04:29 -05:00
osobhandClaude Opus 5.5 bba1560416 fix(format): lay Fixed/Extensible Array chunk indexes out by max dims
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]>
2026-09-25 21:04:19 -05:00
osobhandClaude Opus 5.5 b36998ef01 fix(format): refuse object header messages over 64 KiB
A v2 object header message has a 2-byte size field. The writer truncated
larger sizes to 16 bits, so an attribute over ~64 KiB (or a compact
dataset of 65532-65535 bytes, whose layout message adds 4 bytes) produced
a file libhdf5 rejects ("message of unshareable class flagged as
shareable", "bad flag combination").

ObjectHeaderWriter::serialize now returns a Result and fails on any message
over MAX_MESSAGE_SIZE; FileWriter::finish propagates it. Compact storage
falls back to contiguous above 65531 bytes, the real limit. Dense storage
for large attributes remains future work.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:04:07 -05:00
osobhandClaude Opus 5.5 aef8e766ae fix(format): write and read the registered HDF5 LZ4 filter format
Filter 32004 chunks were framed as a 4-byte little-endian size plus one
LZ4 block. That is not the registered HDF5 LZ4 format (H5Zlz4.c: 8-byte
big-endian total size, 4-byte big-endian block size, then per block a
4-byte big-endian compressed length and the block, stored raw when the
length equals the block size), so libhdf5 + hdf5plugin could not read
our LZ4 datasets and we could not read theirs (h5ex_d_lz4.h5:
"lz4: 0 is not a valid match offset").

Write the registered format (cd_values[0] is honoured as the block size,
default 1 GiB like the plugin) and read it, multi-block and raw blocks
included. Chunks in the old framing stay readable: an HDF5 chunk is under
4 GiB, so a registered chunk always starts with four zero bytes and is at
least 12 bytes long, while an old one starts with four zero bytes only
when empty (5 bytes).

Tests: lz4_reads_registered_hdf5_format (chunk of the HDF Group's
h5ex_d_lz4.h5, block size 3), lz4_writes_registered_hdf5_format,
lz4_reads_legacy_clawhdf5_format, and hdf5plugin_reads_our_lz4 (ignored
interop test; failed before with "filter returned failure during read").

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:03:49 -05:00
osobhandClaude Opus 5.5 9ea44d473d fix(format): read v1 chunk B-tree key offsets as 8 bytes
A type-1 (raw data chunk) B-tree key holds the chunk size, the filter
mask and one offset per dimension, and those offsets are always 8 bytes:
they are dataset coordinates, not file addresses. The reader used the
superblock's size-of-offsets for them, so in a file with 4-byte offsets
every key was misparsed. Unfiltered chunked datasets read as zeros (with
stray bytes where a misread address landed on data) and filtered ones
failed with "deflate: truncated stream".

Only the sibling and child addresses follow size-of-offsets now. The
unit-test B-tree builder wrote keys the same wrong way, which is why its
tests passed; it now matches the format.

Regression: h5py_four_byte_offsets_chunked_reads (h5py, set_sizes(4, 4)
and (4, 8); 1-D and 2-D, unfiltered and gzip) and the unit test
collect_chunks_with_four_byte_addresses.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 21:01:57 -05:00
osobhandClaude Opus 5.5 46203ea761 test(format): keep the 2026-09-20 B-tree v2 fuzz crash as a regression
An 82-byte fuzz_btree_v2 crash input from 2026-09-20 was left untracked
in fuzz/artifacts. Replayed today it runs cleanly: the depth cap and
record budget added to B-tree v2 traversal that day fixed it. It is now
in the committed fuzz corpus, and a robustness test replays the fuzz
target's exact code path on it so a regression fails CI rather than
waiting for someone to run the fuzzer.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 20:38:34 -05:00
osobh 75bdb53342 Merge pull request 'Withdraw the ZeroClaw integration claims' (#10) from docs/withdraw-zeroclaw into main
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Reviewed-on: #10
2026-09-25 19:25:18 +00:00
osobhandClaude Opus 5.5 dd5b3f6633 docs: ClawBrainHub is the one verified consumer
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The previous commit said clawhdf5 has no integration at all. ClawBrainHub
(clawverse/clawbrainhub) does use it: cbh-core reads and writes .brain
files through the facade, cbh-scanner uses the facade, and cbh-cli uses
clawhdf5_agent::bm25::BM25Index, all via path dependencies on this repo.
Checked on 2026-09-25 against main: it builds on its pinned toolchain and
its 204 tests pass. CLAUDE.md now records that, and that path
dependencies mean API changes here reach it directly.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 11:18:14 -05:00
osobh 79dfa78e8f Merge pull request 'Withdraw the OpenClaw integration claims' (#9) from docs/withdraw-openclaw into main
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Reviewed-on: #9
2026-09-25 16:14:36 +00:00
osobhandClaude Opus 5.5 87d64588e5 docs: withdraw the ZeroClaw integration claims
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CLAUDE.md said ZeroClaw "imports this as a Cargo feature (clawhdf5
feature flag)" and uses clawhdf5 as its memory backend; the agent crate
called itself the "ZeroClaw agent memory HDF5 backend"; the migrator
claimed to read "the ZeroClaw layout". Checked on 2026-09-25 against
ZeroClaw v0.8.5 (its latest release), the osobh/zeroclaw fork (on
v0.8.5) and both histories back to February 2026:

- no `clawhdf5` feature, dependency or memory backend has ever existed
  in ZeroClaw; its backends are sqlite, lucid, postgres, qdrant,
  markdown and none, behind its own `Memory` trait;
- ZeroClaw's SQLite schema is a single `memories` table (id, key,
  content, category, embedding, created_at, updated_at); the
  migrator's memory_chunks/sessions/entities/relations layout never
  existed in ZeroClaw, so it cannot read a ZeroClaw database.

Decision: withdraw the claims (as with OpenClaw); clawhdf5 is a
standalone library with no framework integration. The migrator's
default layout is documented as its own. ZEROCLAW_VERSION keeps its name
and value (it is the persisted `edgehdf5_version` writer tag) with a
doc comment saying it is unrelated to ZeroClaw.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 11:08:41 -05:00
osobh bdadf3447c Merge pull request 'Ed25519-signed checkpoints; remove the no-op agent feature' (#8) from feat/signed-checkpoints into main
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Reviewed-on: #8
2026-09-25 15:58:19 +00:00
osobhandClaude Opus 5.5 0c65a27b00 docs: withdraw the OpenClaw integration claims
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The docs described a "drop-in" OpenClaw memory backend enabled with
`memory.backend = "clawhdf5"`. Checked against OpenClaw's source and
docs (v2026.2.26 through v2026.9.6): that config was never valid —
v2026.2-v2026.7 accepted only "builtin"/"qmd" and rejected unknown
keys, so a Gateway given it refuses to start, and v2026.8.1 (OpenClaw
2.0) removed the key. No plugin was ever built (no manifest, no
registration, no tools), nothing was tested against OpenClaw, the
linked github.com/redclawsystems/openclaw is a 404, and
@redclaw/clawhdf5 was never published.

Decision (2026-09-25): not pursuing an OpenClaw plugin for now; ZeroClaw
is the integration target.

- Remove openclaw-integration.md, openclaw-config.md and
  migration-guide.md; add docs/openclaw.md: the status, what a memory
  plugin needs against v2026.9.6 (plugins.slots.memory, manifest with
  kind "memory", registerMemoryCapability / MemorySearchManager,
  prebuilt native packages), and what this repo has as building blocks.
- README, QUICKSTART, USE_CASES, ROADMAP (Track 7 withdrawn), CLAUDE.md
  and the `openclaw` module docs describe ClawhdfBackend as what it is:
  a Markdown-oriented library backend, not an OpenClaw plugin. The
  QUICKSTART example is corrected (the old one called a three-argument
  create that does not exist) and states its limits.
- packages/clawhdf5-node: marked unpublished and broken, "private": true
  so it cannot be published by accident; its bugs (snake_case vs
  camelCase fields, wrong addon path, no way to store an embedding,
  wrong WAL name) are recorded in docs/known-issues.md.
- Two broken rustdoc links fixed along the way.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 10:27:54 -05:00
osobhandClaude Opus 5.5 db9af7972c feat(agent): Ed25519-signed checkpoints
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Makes the README's "cryptographically verifiable memory" true.

With HDF5Memory::set_signing_key(key), every checkpoint stores a signed
manifest of the store: a SHA-256 per memory record (text, embedding as
stored, channel, timestamp, session, tags, deleted flag, activation) in
a Merkle tree, plus hashes of the settings (and WAL mark), sessions and
knowledge graph. The signature, public key and manifest hashes go in
/meta; the per-record hashes in /integrity/record_hashes, so
HDF5Memory::verify(path, &public_key) can say which records changed, not
just that something did. A forged manifest fails the signature.

Decisions, as agreed:
- the key is set on the open store and never persisted;
- a signed store refuses to checkpoint without its key
  (MemoryError::SigningKeyRequired); remove_signature() is the
  deliberate way back to unsigned;
- checkpoints only: saves still in the WAL are not covered, and verify
  reports how many there are.

The hashes cover exactly what the file persists, in the form the loader
returns it (strings lose trailing NULs; an empty WAL mark is not
written), so untouched stores verify across any number of reopen and
checkpoint cycles. MemoryError becomes #[non_exhaustive] (it already
gains variants in this unreleased version).

CLI: keygen (owner-only key file), --signing-key / CLAWHDF5_SIGNING_KEY
on writing commands (create signs immediately), verify --public-key
(JSON; exit 2 if not valid), `signed` in create/stats output.

Tests: reopen/checkpoint cycles with awkward strings (f16 and f32),
refusal without the key, wrong and rotated keys, eight kinds of edit
each detected and located, a forged manifest, unsigned stores, NULs in
text, and an edit made in place with h5py that verify pinpoints.

Cost on tank (search_harness --signing-study --full, 3 runs): ~20% of a
checkpoint (+9 ms at 10K, +89-112 ms at 100K), verify 18.6 ms / 247 ms,
32 bytes per record in the file. New deps ed25519-dalek, sha2,
rand_core: pure Rust, the no-C check passes.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 10:13:34 -05:00
osobh 7706697feb Merge pull request 'Complete the consolidation benchmark: cheaper novelty scoring' (#7) from feat/consolidation-scaling into main
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Reviewed-on: #7
2026-09-25 15:05:58 +00:00
osobhandClaude Opus 5.5 4ecac65f22 chore(agent)!: remove the no-op agent feature
It enabled nothing — the agent layer is always built — yet the README,
QUICKSTART and USE_CASES told people to pass it. Removed, with those
snippets fixed: they now depend on the git repository (nothing is on
crates.io, so `version = "2.0"` never resolved) and USE_CASES no longer
presents the `float16` feature as half-precision storage (that is
MemoryConfig::float16, on by default for new stores).

Breaking for anyone passing `features = ["agent"]`: drop it.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 09:53:53 -05:00
osobh c0f704c381 Merge pull request 'clawhdf5-migrate writes real agent stores; knowledge-graph fix; dated benchmark re-run' (#6) from feat/migrate-and-benchmarks into main
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Reviewed-on: #6
2026-09-25 14:52:35 +00:00
osobhandClaude Opus 5.5 00b0cb0035 perf(agent): cheaper novelty scoring; complete the consolidation benchmark
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consolidation_efficiency never finished: stopped after 19 minutes on one
core while building its 100K case. Not the consolidation cycle (linear:
17 us at 100 records, 2.16 ms at 10K) but the setup — every add_memory
scores the new record's novelty against the whole working tier, the
benchmark lets that tier reach 50K, and each comparison recomputed both
norms: ~5e9 comparisons of three passes each.

ImportanceScorer::score_surprise now computes the new record's norm
once, takes each comparison in one fused, 8-lane pass (dot product and
the other norm together), and splits a working tier of 4096+ records
across threads with the `parallel` feature. Same results: tested against
the old cosine formula, including shorter, empty and zero vectors and
the parallel path. The work stays quadratic in the working-tier size by
design; with regular consolidation the tier stays near
working_capacity (100) and inserts are cheap.

The complete run takes 8 min 10 s on tank and fills in the 100K cycle
row (46.66 ms) and the memory-reduction table, which had never been
published. The binary no longer prints a record-count ratio as a
"BM25 Speedup" (never measured; Part 1 measures search latency) or
claims sub-linear cycle scaling (its own numbers grow slightly faster
than linearly).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 08:51:08 -05:00
osobh a7920bd4b3 Merge pull request 'Search options (source filters, re-ranking, confidence); float16 default' (#5) from feat/search-options into main
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Reviewed-on: #5
2026-09-25 12:44:15 +00:00
osobh 7e43b5366c Merge branch 'main' into feat/search-options
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2026-09-25 12:32:00 +00:00
osobhandClaude Opus 5.5 dce5559ff2 bench: re-run every stale BENCHMARKS.md section, dated and traced
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Every undated or pre-September section re-run on one machine on one day
(tank, AMD Ryzen 7 7800X3D, 2026-09-24, commit 5c8323c), 24 commands run
serially with the load average checked before each, with the command
recorded for each section. A separate check traced every changed number
back to the raw output; its corrections are applied (e.g. the on-disk
~820 B/record is float16 plus always-deflated text on a synthetic corpus
of 40 distinct texts, not float16 alone).

Two apparent regressions were isolated rather than published:
- knowledge-graph traversal: a real bug, fixed in the previous commit;
- the write path: v2.3.0 built and run on the same machine measures the
  same as today, so the old 18 us / 6.17 ms figures (undated, other
  hardware) are not reproducible; float16 adds ~2 us per save and the
  int8 index nothing (both isolated by switching the bench's config).

Also:
- new multimodal_bench: cross-modal search at 1K/10K records, which the
  README claimed but nothing measured;
- footprint_bench reports whether it built float16 or f32 stores and
  takes --f32 (it kept printing "f32" after the default changed);
- README: performance tables, the "Why" table figures and the SQLite
  migration section (from the previous migrate commit);
- CHANGELOG for this branch.

Not re-run: consolidation_efficiency's 100K row and its memory-reduction
part (stopped for time), and cross_platform.sh.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 23:45:15 -05:00
osobhandClaude Opus 5.5 1b3bbb054a perf(agent): cache the knowledge graph's adjacency index
bfs_neighbors and spreading_activation built an adjacency index over the
whole graph on every call (1efd82c), so a 2-hop BFS over 1K entities
paid to index every entity and relation first: 155 us, 6.5x the 24 us
the README quoted. Found by the dated benchmark re-run.

The index is now cached on KnowledgeCache and checked against a
fingerprint of the graph on each use — one pass over entity ids and
relation endpoints, no allocation — so any change, including direct
edits of the public entities/relations Vecs (schema.rs's load path
pushes to them), still triggers a rebuild. A test edits the graph
directly in every way (push, in-place rewire, pop + push at equal
length) between traversals.

tank, 2026-09-24: BFS 1K entities 155.1 -> 23.1 us, 100 entities
17.5 -> 5.23 us, spreading activation 100 22.8 -> 10.1 us.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 23:45:15 -05:00
osobhandClaude Opus 5.5 a8fb758489 fix(migrate): write a real clawhdf5-agent store
clawhdf5-migrate wrote a layout of its own (/chunks, /sessions,
/entities, /relations, root attributes, no /meta or schema_version) that
HDF5Memory::open rejects, so a "migrated" SQLite database could not be
used as agent memory — contrary to the README.

It now writes through the agent's own API (HDF5Memory::create/open,
save_batch, the session cache and the knowledge graph), so there is no
second copy of the schema:

- sessions and entities/relations carry over; deleted rows become
  deleted records (or are left out with --skip-deleted);
- embeddings follow the library default (float16), --f32 opts out and
  --float16 is a hidden no-op, as in clawhdf5-cli; the `half`-based
  conversion is gone;
- every source row is checked before the output is created: a wrong
  embedding length, an empty embedding, a dimension that differs from
  an existing store's, or a float16 value beyond +-65504 is an error
  naming the chunk id, and an existing store is left untouched;
- --incremental opens the existing store, adds only rows it does not
  hold (matched by content) and follows the source's deleted flags;
- a source with no memory rows needs --embedding-dim;
- validation reads the result back with HDF5Memory::open_read_only,
  compares every field (embeddings bit for bit, round_to_f16 of the
  source for float16) and checks a migrated record is found by search.

clawhdf5-agent gains HDF5Memory::sessions()/sessions_mut(),
HDF5Memory::delete_batch (one save, all-or-nothing, no auto-compact),
SessionCache::add_at, and re-exports SessionCache/SessionEntry.

The old layout's per-dataset SHA-256 provenance attributes have no place
in the agent schema and are gone. An adversarial review found two
blockers (silent truncation of long embeddings; an --incremental
dimension check that could never fire) and four majors (a failed run
wiping the existing store, dim-0 stores, deleted-flag drift); all are
fixed with regression tests. 42 migrate tests, incl. h5py opening a
migrated store.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 23:45:15 -05:00
osobh 73bb068264 Merge pull request 'Files open in h5py again; float16 embedding storage' (#4) from feat/float16-embeddings into main
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Reviewed-on: #4
2026-09-25 03:18:07 +00:00
osobhandClaude Opus 5.5 5c8323cb1e feat(agent): new stores default to float16 embeddings
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MemoryConfig::float16 now defaults to true for new stores, on
measurement: on the full LongMemEval haystack with real MiniLM
embeddings every retrieval metric matched f32 (previous commit), and at
100K the file is 48% smaller with faster checkpoints and opens.

Existing stores are unaffected: every agent store has recorded
`float16 = false` in /meta and keeps it. A test opens the v2.5.0
fixture, saves and checkpoints, and checks the embeddings are still f32
with the old rows bit-identical; another checks a new store is float16.

CLI: `create --f32` opts out; like `--f32-index` it only ever switches
the default off. `--float16` is still accepted and now a no-op.
Values beyond +-65504 are refused, so f32 remains the choice for
unnormalised vectors — the upgrade note says so.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 19:39:33 -05:00
osobhandClaude Opus 5.5 dbaf3f505d bench(longmemeval): --float16, and float16 measured on real embeddings
`longmemeval_bench --float16` builds every per-question store with
MemoryConfig::float16, so the vector stage searches half-rounded
embeddings exactly as such a store holds them.

Full longmemeval_s (500 questions, ~494 turns each) with real
all-MiniLM-L6-v2 embeddings, f32 vs float16, on tank (CUDA): identical
at every Hit@k and MRR, turn and session level, in all eight modes —
bar RRF session MRR 0.9253 vs 0.9254 and one or two flips out of ~320
in which gold session ranks first. The f32 run reproduces the published
hybrid numbers exactly. The earlier float16 evidence was synthetic
clustered data only; this is the real-embedding check.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 19:21:14 -05:00
osobhandClaude Opus 5.5 c470244a6f feat(agent): HDF5Memory::search with source filters, re-ranking, confidence
`HDF5Memory::search(query_embedding, query_text, &SearchOptions)` is the
store's full search path. `SearchOptions::new(k)` is plain hybrid search
with the tuned default fusion; each further stage is opt-in:

- `with_sources([..])`: only records from these source channels. The
  filter applies before ranking, so a filtered search still returns up
  to k results, normalised over what it can return. The HNSW pool is
  over-fetched in proportion to what the filter removes, and the allowed
  records are scanned exactly whenever that costs fewer distance
  evaluations than the index would (~pool x M) — and as the fallback if
  the pool comes back short. Keyword matches are filtered too.
- `with_rerank(ReRankConfig)` re-ranks a max(3k, 10) candidate pool by
  relevance, recency, source authority and activation;
  `with_confidence(ConfidenceConfig)` drops low-confidence results;
  `at_time(now)` pins the recency clock.

These were reachable only through the OpenClaw backend, which is now
`search` with both on. Its Hebbian boost now goes to the k results it
returns rather than the whole 3k candidate pool. `hybrid_search` and
`hybrid_search_with` are wrappers and unchanged (tested bit for bit).

Measured on tank (search_harness --options-study --full, 3 runs): at
100K every filter — 50%, 10%, 1% of the store, and records far from the
query — returns the exact filtered top 10, and none is slower than an
unfiltered search (1%: 2.3 ms vs 4.6 ms). Re-rank + confidence costs
about 3%. A first version decided between index and exact scan by pool
size vs store size; it measured 0.976 recall at 12.3 ms on the
far-from-query filter, which is why the rule compares costs instead.

Tests: tests/search_options.rs (filter correctness and full pages via
both paths, far-from-query fallback, edge cases, equality with
hybrid_search_with, re-rank recency, confidence, boost scope).

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-24 16:35:42 -05:00
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
osobh 1cceb930b2 Merge pull request 'Feat/pure rust default and msrv' (#3) from feat/pure-rust-default-and-msrv into main
CI / test-arm64 (push) Successful in 1m20s
CI / test (push) Successful in 5m59s
Reviewed-on: #3
2026-09-23 16:29:33 +00:00
osobhandClaude Opus 5.5 fc7ae6549a build: declare Rust 1.92 as the MSRV and check it in CI
CI / test-arm64 (pull_request) Successful in 1m19s
CI / test (pull_request) Successful in 5m23s
rust-version = "1.92" in [workspace.package], inherited by every crate.
1.92 is the floor: wgpu (clawhdf5-gpu) requires it, and the whole
workspace, Python bindings included, checks cleanly on it. ci-test.sh
reads the version from Cargo.toml and checks the workspace on exactly
that toolchain, so the manifests and the README badge cannot drift from
what actually builds. The badge said 1.75, below edition 2024's own
floor.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-23 11:05:26 -05:00
osobhandClaude Opus 5.5 735db117a7 build: pure-Rust zlib-rs as the default deflate backend
The core crates (clawhdf5, -agent, -format, -io, -filters, -ann, -accel,
-netcdf4, -cli) now build no C by default: deflate defaults to zlib-rs,
a pure-Rust port of zlib-ng, and zlib-ng becomes the opt-in
`fast-deflate`, which overrides zlib-rs wherever it is enabled. A default
build no longer needs cmake or a C compiler.

Measured on tank, both builds run alternately, three rounds, medians:
zlib-rs is within 6% of zlib-ng on every HDF5 read and write (512x512
deflate-6 chunked write 1.458 vs 1.484 ms; 64 MB compressed read 64.4
vs 65.2 ms), and compressed output is byte-identical. Details in
BENCHMARKS.md, "Deflate backend".

Getting there took two fixes the first measurement exposed:

- zlib-rs needs `std` to detect SIMD at runtime. flate2 enables it via
  its default `runtime_detection`, which `default-features = false` had
  switched off, leaving zlib-rs 3.5x slower on inflate. The `zlib-rs`
  features now enable it.
- Both deflate paths streamed through flate2's 32 KiB read/write
  wrappers. They now hand the codec the whole chunk in one call, into a
  buffer sized up front (~5% on chunked writes). This also fixes a
  silent short read: the streaming reader returned a truncated stream's
  bytes without an error; a truncated chunk is now DecompressionError.
  In clawhdf5-filters, output longer than the stated size is now an
  error rather than silently cut off.

CI: ci-test.sh lints and tests the zlib-ng path, and fails if a
C-building crate (*-sys, cc, cmake) enters a core crate's default
dependency tree. The arm64 job no longer installs cmake.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-23 11:05:22 -05:00
osobhandClaude Opus 5.5 e9b37a9602 docs: bring the README up to date with the last five releases
The README had fallen behind v2.3.0-v2.7.0, and parts of it were not
true. Checked every claim against the code and BENCHMARKS.md:

- Three of the six Quick Start snippets no longer compiled (Agent
  Memory, Consolidation, OpenClaw); all six now do.
- Hybrid search was described as RRF throughout. The default has been
  weighted 0.4/0.6 fusion since v2.5.0; re-ranking and confidence
  rejection run only in the OpenClaw backend.
- The `float16` feature does not halve embedding storage (the store
  always writes f32), `--features agent` enables nothing, "Source
  Isolation" is not wired in, and nothing backs "billion-scale" IVF-PQ.
- "Cryptographically verifiable" overstated an unkeyed, session-scoped
  FNV-1a ledger; "Zero C dependencies" was false while zlib-ng was the
  default deflate backend.
- Stale numbers: tests (1,650 -> 1,868), Rust badge (1.75 is below
  edition 2024's floor), 6.5 KB/record on disk (BENCHMARKS.md: 1.7 KB),
  consolidation and hybrid-search latency, and a feature-flag table
  broken by a paragraph pasted into it.
- The file schema, module table and crate map now match the code.

Adds a "What's new (v2.2 -> v2.7)" section for collaborators, leading
with the silent Extensible Array read bug fixed in v2.7.0. Footer links
point at git.redclaw.dev. CLAUDE.md: clawhdf5-migrate is the SQLite
migration tool, and MemoryConfig::compression is off by default.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-23 11:05:10 -05:00
osobhandClaude Opus 5 4bed8b3765 Merge docs/ci: record how CI is set up
CI / test-arm64 (push) Successful in 1m6s
CI / test (push) Successful in 3m28s
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-22 04:48:17 -07:00
osobhandClaude Opus 5 36d689bc2c docs: record how CI is set up, and what broke it
Two jobs, which runners serve them, and the two constraints that kept
the x86 job failing on every push until today: no JavaScript actions
(`rust:latest` has no `node`, and GitHub is not reachable from every
runner) and `cmake` for libz-ng-sys. Also notes that the Docker Hub
`latest` tag for the runner is frozen at 0.6.1, so it is not a way to
stay current.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-22 04:48:17 -07:00
osobhandClaude Opus 5 e7c08e06b4 Merge ci/fix-jobs: make both CI jobs actually run
CI / test-arm64 (push) Successful in 1m55s
CI / test (push) Successful in 4m8s
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 20:37:51 -07:00
osobhandClaude Opus 5 c5049eb734 ci: stop depending on node, and install cmake where the build needs it
Read from the job logs of the first run with the arm64 job, rather than
guessed:

- The x86 `test` job has been failing on every push, in about three
  seconds. It runs in `rust:latest` and starts with actions/checkout, a
  JavaScript action, and `rust:latest` has no `node`: exit 127 before a
  line of code was built. It is replaced with a plain git checkout (the
  image has git), and actions/cache, JavaScript for the same reason, is
  dropped.
- `test-arm64` got through checkout, toolchain, the aarch64 check and
  clippy, then failed building libz-ng-sys — pulled in by
  clawhdf5-format's default `fast-deflate` — because the host runner had
  no cmake. The x86 image lacks it too, so the x86 job would have hit the
  same wall one step later.

Both jobs now install cmake where they can (the Docker job and the x86
container run as root) and say plainly when they cannot (a host runner),
instead of failing inside a build script. vision-01 now has cmake.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 20:37:51 -07:00
osobhandClaude Opus 5 6f6bc97850 Merge ci/arm64: run the aarch64 kernels in CI
CI / test (push) Failing after 3s
CI / test-arm64 (push) Failing after 37s
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 20:31:05 -07:00
osobhandClaude Opus 5 0cb72e8a60 ci: test the aarch64 kernels on an arm64 runner
The NEON kernels in clawhdf5-accel — `dot_i8` including its SDOT path,
and the f32 NEON kernels that predate it — are cfg'd out on x86, so the
existing job has never compiled, linted or tested a line of them. They
were verified once, by hand, on a Raspberry Pi 5.

`test-arm64` runs on `linux_arm64`, which two runners serve in different
ways: vision-01 executes steps on the host with Rust preinstalled, and
vision-02 executes them in docker.gitea.com/runner-images. The job is
written to work in both: no `container:`, no JavaScript actions (those
are fetched from GitHub, which not every runner reliably reaches), and
an explicit `+stable` toolchain so a host's default — vision-01's is a
January nightly — is neither relied on nor changed. Fetches retry, since
one runner's outbound network was seen failing intermittently.

It lints the accel crate and tests accel, ann and format. It reports
rather than requires the dot-product extension: on a core without it the
plain-NEON kernel is the one that runs, and the tests cover whichever is
present.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 20:31:05 -07:00
osobhandClaude Opus 5 e338d58ad5 Merge feat/int8-default: new stores use the int8 index
CI / test (push) Failing after 2s
quantized_index defaults to true for new stores — smaller and faster at
equal recall on every configuration measured. Existing stores keep
their setting, and stores predating it stay f32, guarded by a real
v2.5.0 store committed as a test fixture.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 18:27:59 -07:00
osobhandClaude Opus 5 8b85d9364b feat(agent): new stores use the int8 vector index by default
`MemoryConfig::quantized_index` now defaults to `true`. It holds a
quarter of the index memory and, with the exact re-score, is faster at
equal recall on every configuration measured: 1.63x the queries per
second on x86-64 (AVX2) and 1.18x on a Raspberry Pi 5 (NEON SDOT), with
builds 1.8x and 2.3x faster. The one argument for keeping it off — that
int8 search was slower on ARM — did not survive being measured.

Existing stores do not change. A store written with v2.6.0 or later
keeps its persisted setting. One written before the setting existed has
no stored value, and it loads as `false` rather than as the new default,
so reopening it never changes how its index is held. That case is
guarded by a real store written with the v2.5.0 CLI, committed as
`tests/fixtures/store_v2_5_0.h5` (6.8 KB): the test asserts it reopens
with an f32 index and still searches, and it fails if the load default
is changed to `true`.

The CLI needed more than a new default. `create --quantized-index`
assigned its value straight into the config, so under the new default
every CLI-created store would have been forced back to f32 unless the
caller knew to ask for int8. It is replaced by `--f32-index`, which only
ever switches the default off; `--quantized-index` is still accepted,
hidden, as a no-op, and the two conflict.

The whole agent suite passes under the new default, including the
brute-force recall oracle, now running on int8 plus re-score without
being asked to.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 18:27:59 -07:00
osobhandClaude Opus 5 6598a7d02f Merge feat/neon-int8: aarch64 int8 dot product, verified on a Pi 5
CI / test (push) Failing after 2s
SDOT and plain-NEON kernels for dot_i8, tested bit-exact against scalar
on real ARM. At equal recall the quantised index is 1.18x f32 on a Pi 5
and builds 2.3x faster. Also corrects an unmeasured claim that it was
slower than f32 on ARM.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 17:34:51 -07:00
osobhandClaude Opus 5 114a2dfcba docs: measured ARM numbers, and a correction
On a Raspberry Pi 5 at N = 100 000 and equal recall (0.9940 vs 0.9945),
medians of three runs:

  f32             33 413 ms build   6 164 QPS
  int8 scalar     18 950 ms         ~6 190 QPS   (what v2.7.0 shipped)
  int8 NEON      ~17 000 ms          6 640 QPS
  int8 SDOT       14 464 ms          7 267 QPS   1.18x f32, 2.3x build

The docs said quantised search stayed off by default because aarch64
"falls back to the scalar loop, where the original trade still
applies" — that it was ~13% slower than f32 there, as on x86. That was
extrapolated rather than measured, and it was wrong: x86's portable
baseline is SSE2 against hand-written AVX2 f32 kernels, but on aarch64
NEON is the baseline and the scalar loop vectorises well, so it already
matched f32. Corrected in BENCHMARKS.md, README.md and CLAUDE.md; the
released v2.7.0 changelog entry is left as it was and the correction is
recorded in a new one.

Labelled as Pi 5 figures throughout — a Pi's memory bandwidth and cache
are far below an M-series or flagship phone, so the ratios will move.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 17:34:51 -07:00
osobhandClaude Opus 5 56a8c2f3d0 feat(accel): aarch64 int8 dot product — SDOT and plain NEON
`dot_i8` had an AVX2 kernel and a scalar fallback, so on aarch64 the
quantised HNSW index ran the scalar loop. It now dispatches to one of
two NEON kernels:

- `dot_i8_dotprod`: the ARMv8.2 dot-product instruction, `SDOT`, which
  multiplies and accumulates sixteen i8 pairs into four i32 lanes per
  instruction. Present on Cortex-A76 and later (Raspberry Pi 5, current
  Android phones), Neoverse-N1 (Graviton2, Ampere Altra) and every Apple
  Silicon generation. Issued as inline assembly because the `vdotq_s32`
  intrinsic is still behind the unstable `stdarch_neon_dotprod` feature;
  inline asm is stable on aarch64.
- `dot_i8`: plain NEON for cores without the extension — `vmull_s8`
  widens to i16 (even -128 * -128 fits) and `vpadalq_s16` folds adjacent
  pairs into i32 accumulators, so nothing overflows.

Selected at runtime with `is_aarch64_feature_detected!("dotprod")`.

Verified on a Raspberry Pi 5 (Cortex-A76, `asimddp` present), not just
compiled — the aarch64 code is cfg'd out on x86, so x86 CI never builds
or lints it:

- both kernels bit-exact against scalar at every length, tails and
  extremes included. Each is tested directly rather than through
  dispatch, because dispatch only takes one path on a given CPU: on the
  Pi, testing through it alone would never have run the plain-NEON
  fallback at all.
- mutation-checked: dropping the SDOT kernel's second accumulator fails
  at length 32, and using the low half twice in the NEON kernel fails at
  length 16 — the first lengths that exercise each.
- the ANN suite passes, including int8 recall against ground truth.
- clippy clean with -D warnings on aarch64.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
2026-09-21 17:34:51 -07:00
180 changed files with 23447 additions and 4645 deletions
+52 -12
View File
@@ -9,15 +9,15 @@ jobs:
runs-on: ubuntu-latest
container: rust:latest
steps:
- 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') }}
# 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 "${GITHUB_SHA}" && break; sleep 5; done
git checkout -q FETCH_HEAD
- name: Install rustfmt & clippy components
run: rustup component add rustfmt clippy
- name: Install thumbv7em-none-eabihf target
@@ -28,12 +28,15 @@ jobs:
# dependency a failure (CLAWHDF5_REQUIRE_INTEROP below).
run: |
apt-get update
apt-get install -y --no-install-recommends python3 python3-venv
# cmake builds libz-ng-sys for the opt-in `fast-deflate` (zlib-ng)
# steps in ci-test.sh; rust:latest does not ship it. The default
# build (pure-Rust zlib-rs) does not need it.
apt-get install -y --no-install-recommends python3 python3-venv cmake
python3 -m venv /opt/interop
/opt/interop/bin/pip install --no-cache-dir h5py numpy netCDF4 xarray
/opt/interop/bin/pip install --no-cache-dir h5py numpy netCDF4 xarray hdf5plugin
echo "/opt/interop/bin" >> "$GITHUB_PATH"
- name: Show interop library versions
run: /opt/interop/bin/python -c "import h5py, netCDF4; print('h5py', h5py.__version__, 'HDF5', h5py.version.hdf5_version, 'netCDF4', netCDF4.__version__)"
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 $GITHUB_PATH
@@ -44,3 +47,40 @@ jobs:
CLAWHDF5_PYTHON: /opt/interop/bin/python
CLAWHDF5_REQUIRE_INTEROP: "1"
run: bash scripts/ci-test.sh
test-arm64:
# The aarch64 kernels in clawhdf5-accel — NEON `dot_i8`, including the
# SDOT path, and the f32 NEON kernels — are cfg'd out on x86, so the job
# above never compiles, lints or tests them.
#
# `linux_arm64` is served by two runners that execute differently:
# vision-01 runs steps on the host (Rust already installed) and vision-02
# runs them in docker.gitea.com/runner-images. So the steps work in both:
# no `container:`, no JavaScript actions (they are fetched from GitHub,
# which not every runner reliably reaches), and an explicit `+stable`
# toolchain rather than whatever a host happens to default to.
runs-on: linux_arm64
env:
CARGO_NET_RETRY: "10"
CARGO_TERM_COLOR: always
steps:
- name: Check out
run: |
git init -q .
git remote add origin "${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 "$(uname -m)" = aarch64
if grep -q asimddp /proc/cpuinfo; then echo "dot-product extension present: SDOT kernel runs"; else echo "no dot-product extension: plain NEON kernel runs"; fi
- name: Clippy (aarch64 kernels)
run: cargo +stable clippy -p clawhdf5-accel --all-targets -- -D warnings
- name: Test
run: cargo +stable test -p clawhdf5-accel -p clawhdf5-ann -p clawhdf5-format
+56
View File
@@ -0,0 +1,56 @@
name: Conformance
# Nightly: read every file of the pinned public HDF5 corpora with clawhdf5 and
# with h5py/libhdf5 and compare (conformance/run.sh; CONFORMANCE.md explains
# the method). Fails on any panic, hang, crash or out-of-memory in clawhdf5,
# and when the ok count drops below conformance/baseline.json or a file the
# baseline lists as ok stops being ok. The report is printed into the job log;
# nothing is uploaded (artifact actions are JavaScript, which rust:latest
# cannot run — see CLAUDE.md).
on:
schedule:
- cron: "17 3 * * *"
workflow_dispatch:
jobs:
conformance:
runs-on: ubuntu-latest
container: rust:latest
timeout-minutes: 60
env:
CARGO_NET_RETRY: "10"
steps:
# Plain git, not actions/checkout (a JavaScript action; see ci.yml).
- name: Check out
run: |
git init -q .
git remote add origin "${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
+864 -144
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+563
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@@ -1,5 +1,568 @@
# Changelog
## Unreleased
### Upgrade Notes
- **HDF5 correctness audit (2026-09-25).** A sweep of 686 public files (the
libhdf5 test files, the HDF Group's CVE reproducers, pyfive, netcdf-c,
netcdf4-python, h5wasm, h5py and xarray corpora), a 567-case read matrix and
a 96-case write matrix against HDF5 1.10–2.0 found bugs that returned wrong
values with no error, and files we wrote that libhdf5 rejects. The fixes are
listed under Correctness and Interop. What changes for callers:
- **Chunked datasets whose max shape is larger than their current shape**,
or whose unlimited dimension is not the first, were indexed by the current
shape instead of the max shape, both when read and when written. Files from
libhdf5 now read correctly. Files clawhdf5 wrote with such a max shape were
laid out wrongly and now read the way libhdf5 always read them — rewrite
them. Agent stores and ClawBrainHub files have no max shape and are
unaffected.
- Integer reads (`read_i32`/`read_i64`/`read_u64`/...) of float data now
convert (truncate toward zero, saturate at the type's range, NaN reads as
0) instead of returning the IEEE bit pattern, and out-of-range integers
saturate instead of keeping the low bits.
- `FileWriter::finish()` now returns an error instead of writing a corrupt
file for: a header message over 64 KiB (e.g. an attribute larger than
~64 KiB), a group/dataset/link name that is empty, `.` or contains `/`
(nested paths were written as one literal link), a max shape smaller than
the shape, a page size outside 512 B–1 GiB, and more than 65 535 chunks in
a dataset with several unlimited dimensions.
- **Breaking (format crate):** `ObjectHeaderWriter::serialize`,
`BatchObjectHeaderWriter::compute_sizes`/`serialize_all` and
`build_chunked_data_from_precompressed` return `Result`;
`read_fixed_array_chunks`/`read_extensible_array_chunks` take `max_dims`;
`build_fixed_array_at`/`ea_writer::build_extensible_array_at` take one
`Option<WrittenChunk>` per index slot; `fill_value::dataset_fill_value`
returns `UnresolvedSharedMessage` for a shared message it cannot resolve
instead of `None`. `FillTime::default()` is `IfSet` (libhdf5's default;
default files are byte-identical).
- **ZeroClaw does not use clawhdf5.** The project described itself as
ZeroClaw's memory backend ("imported as a `clawhdf5` Cargo feature"). Checked
against ZeroClaw v0.8.5 (the latest release), the `osobh/zeroclaw` fork and
their full history: no such feature or backend has ever existed. And
`clawhdf5-migrate`'s "ZeroClaw layout" (`memory_chunks`, `sessions`,
`entities`, `relations`) is not ZeroClaw's schema — ZeroClaw uses a single
`memories` table — so the migrator cannot read a ZeroClaw database. The
claims are withdrawn; the migrator's layout is documented as its own.
- **OpenClaw is not supported, and never was.** The docs described a
"drop-in" OpenClaw memory backend enabled with `memory.backend = "clawhdf5"`.
That config was never valid in any OpenClaw release (v2026.2–v2026.7
accepted only `builtin`/`qmd` and rejected unknown keys, so a Gateway given
it refuses to start; OpenClaw 2.0 removed the key), no plugin was ever built,
and `@redclaw/clawhdf5` was never published. The integration docs
(`openclaw-integration.md`, `openclaw-config.md`, `migration-guide.md`) are
removed; `docs/openclaw.md` explains the status and what a real plugin would
need against OpenClaw v2026.9.6. `ClawhdfBackend` stays as a library API.
- **Breaking:** `MemoryError` is now `#[non_exhaustive]` and gained
`SigningKeyRequired`; a `match` on it needs a wildcard arm. Future variants
will no longer be breaking.
- **Breaking:** `clawhdf5-agent`'s `agent` feature is removed. It enabled
nothing — the agent layer is always built — but the README and guides told
people to pass it; drop `agent` from `features = [...]`.
- **`clawhdf5-migrate` now writes a real agent store.** Its output used to be
a layout of its own (`/chunks`, `/sessions`, `/entities`, `/relations`, no
`/meta`) that `HDF5Memory::open` rejected, so a migrated file could not be
used as agent memory. Files it wrote before this release are not agent
stores; re-run the migration. Also: embeddings default to `float16` like
any new store (`--f32` opts out; `--float16` is a hidden no-op); a row with
the wrong embedding length is an error instead of being truncated or
padded; `--incremental` now matches rows by content against an existing
store and follows the source's deleted flags; a source with no memory rows
needs `--embedding-dim`. The per-dataset SHA-256 provenance attributes of
the old layout are gone (the agent schema has no place for them).
- **Files written by clawhdf5 now open in h5py and libhdf5.** Every `f32`
dataset we wrote — including every agent store's embeddings — was refused
with "sign bit position out of bounds", and every empty dataset with
"invalid dataset size". Both were write-side bugs present in every release;
clawhdf5's own reader was unaffected. An agent store is rewritten in full at
each checkpoint, so it becomes readable at its next checkpoint on this
version; other files with `f32` or empty datasets need rewriting. Details in
`docs/known-issues.md`.
- **New stores store embeddings as half precision by default.**
`MemoryConfig::float16` was persisted and otherwise ignored; it now writes
`float16` embeddings (48% smaller files at 100K) and rounds each embedding
to half precision as it is saved — and it defaults to `true` for new
stores. On the full LongMemEval haystack with real MiniLM embeddings every
retrieval metric matched `f32`. **Existing stores are unaffected**: every
agent store has recorded `float16 = false`, and keeps it (a v2.5.0 fixture
guards this). A store that already had `float16 = true` rounds its
embeddings when next opened and writes them as `float16` at its next
checkpoint. Opt out with `float16 = false` or `create --f32`; the CLI's
`--float16` is still accepted and now a no-op. Values beyond ±65504 are
refused, so keep `f32` for unnormalised vectors.
- **Breaking:** `MemoryError` gained `InvalidEntry`, returned when a
`float16` store is given an embedding value beyond ±65504. Exhaustive
matches need the new arm.
- **The default build no longer compiles any C.** Deflate now defaults to the
pure-Rust zlib-rs instead of zlib-ng, so building the core crates needs
neither cmake nor a C compiler. Speed on HDF5 reads and writes is within 6%
of zlib-ng, and compressed output is byte-identical. To keep zlib-ng, enable
`fast-deflate` (on `clawhdf5`, `clawhdf5-format` or `clawhdf5-filters`); it
overrides zlib-rs wherever it is on.
- **A truncated deflate chunk is now an error.** It used to read back short,
with no error.
- **Minimum supported Rust is 1.92**, now declared in every crate's
`rust-version` and checked in CI.
- **New stores use the int8 vector index by default.**
`MemoryConfig::quantized_index` now defaults to `true`: a quarter of the
index memory, builds 1.8x (x86-64) and 2.3x (Raspberry Pi 5) faster, and
searches 1.63x and 1.18x faster at equal recall, measured on every
configuration tested. **Existing stores are unaffected** — a store written
with v2.6.0 or later keeps its persisted setting, and one written before the
setting existed opens as `false` and keeps its f32 index. Set
`quantized_index = false`, or pass `create --f32-index` to the CLI, to opt
out. The CLI's `--quantized-index` is still accepted but is now a no-op.
### Signing
- `clawhdf5-agent`: **Ed25519-signed checkpoints** — the README's
"cryptographically verifiable memory", now true. With
`HDF5Memory::set_signing_key(key)`, every checkpoint stores a signed
manifest: a SHA-256 per record (text, embedding as stored, channel,
timestamp, session, tags, deleted flag, activation) in a Merkle tree, plus
hashes of the settings (and WAL mark), sessions and knowledge graph, with
the per-record hashes in `/integrity/record_hashes`.
`HDF5Memory::verify(path, &public_key)` recomputes everything from the file
and reports which part changed and which records (`changed_records`); a
forged manifest fails the signature. The key is never persisted; a signed
store refuses to checkpoint without it (`MemoryError::SigningKeyRequired`),
and `remove_signature()` is the deliberate way back to unsigned. Saves still
in the WAL are not covered (`wal_entries_unsigned`). Tests include every
kind of edit, and an edit made with h5py in place, which verify pinpoints.
Cost: ~20% of a checkpoint, 32 bytes per record (`BENCHMARKS.md`, "Signed
checkpoints"). New dependencies `ed25519-dalek`, `sha2`, `rand_core` — pure
Rust; the no-C check still passes.
- `clawhdf5-cli`: `keygen --out <file>` (owner-only key file),
`--signing-key <file>` / `CLAWHDF5_SIGNING_KEY` on writing commands
(`create` signs immediately), `verify --public-key <hex|file>` (JSON report;
exit status 2 if not valid), and `signed` in `create`/`stats` output.
### Migration
- `clawhdf5-migrate`: writes through the agent's own API (`HDF5Memory::create`
/ `open`, `save_batch`, the session cache and knowledge graph), so there is
no second copy of the schema. Sessions and entities/relations carry over;
deleted rows become deleted records (or are left out with
`--skip-deleted`). Every source row is checked before the output is created,
so a source that cannot be migrated leaves an existing store untouched.
Validation reads the result back with `HDF5Memory::open_read_only`, compares
every field (embeddings bit for bit — `round_to_f16` of the source for a
`float16` store) and checks that a migrated record is found by search. The
`half`-based conversion is gone; `clawhdf5_format::float16` is the only one.
42 tests, including h5py opening a migrated store; an adversarial review's
two blocker and four major findings are fixed with regression tests.
- `clawhdf5-agent`: `HDF5Memory::sessions()` / `sessions_mut()`,
`HDF5Memory::delete_batch(&[usize])` (one save, all-or-nothing, never
auto-compacts), `SessionCache::add_at`, and `SessionCache` / `SessionEntry`
re-exported from the crate root.
### Search
- `clawhdf5-agent`: **`HDF5Memory::search` with `SearchOptions`** — source
filtering, re-ranking and confidence rejection in the store's own search
path. Re-ranking and confidence rejection used to be reachable only
through the OpenClaw backend, which now calls `search` with both on.
- `with_sources([..])` restricts a search to records from those source
channels. It applies before ranking, so a filtered search still returns up
to `k` results, normalised over what it can return. Measured at 100K: the
exact filtered top 10 for filters keeping 50%, 10% and 1% of the store and
for records far from the query, and never slower than an unfiltered search
(2.3 ms for a 1% filter vs 4.6 ms unfiltered). See `BENCHMARKS.md`,
"Search options".
- `with_rerank(ReRankConfig)` re-ranks a pool of `max(3k, 10)` candidates
(`rerank_pool` to change it) by relevance, recency, source authority and
activation; `with_confidence(ConfidenceConfig)` drops low-confidence
results; `at_time(now)` pins the clock for recency. About 3% on latency.
- `hybrid_search` and `hybrid_search_with` are unchanged (tested bit for
bit against `search` with default options).
- `clawhdf5-agent`: the OpenClaw backend's search now boosts the Hebbian
activation of the `k` results it returns, not of the whole `3k` candidate
pool it re-ranks.
### Documentation
- OpenClaw claims withdrawn across the README, QUICKSTART, USE_CASES, ROADMAP
(Track 7 marked withdrawn) and the `openclaw` module docs; the dead
`github.com/redclawsystems/openclaw` link is gone. The Node package is
marked unpublished and broken (now `"private": true` so it cannot be
published by accident), with its bugs recorded in `docs/known-issues.md`.
### Benchmarks
- Every undated or pre-September section of `BENCHMARKS.md` re-run on one
machine on one day (tank, 2026-09-24, commit 5c8323c), with the command for
each and every number traced back to the raw output by a separate check.
Where a figure moved, the section says so. Two apparent regressions were
isolated rather than published: knowledge-graph traversal (a real bug,
fixed above) and the write path, which measures the same at v2.3.0 on this
machine — the old 18 µs / 6.17 ms figures came from an undated run on other
hardware; `float16` adds ~2 µs per save and the int8 index nothing.
- New `multimodal_bench`: cross-modal search at 1K and 10K records, which the
README claimed but nothing measured.
- `footprint_bench` reports whether it built `float16` or `f32` stores and
takes `--f32`; it had kept printing "f32" after the default changed.
### Interop
- **Conformance sweep in the repo** (`conformance/`, report in
`CONFORMANCE.md`). `conformance/run.sh` fetches eight public HDF5 corpora
pinned by commit (libhdf5's test files, the HDF Group's CVE reproducers,
pyfive, netcdf-c, netcdf4-python, h5wasm, h5py, xarray-data) into a
gitignored cache, reads every file with clawhdf5 and with h5py/libhdf5 (and
the CVE files with h5dump) under a timeout and memory limit, compares them
object by object and regenerates the report — about 30 s once the corpus is
cached. A nightly Gitea job (`.gitea/workflows/conformance.yml`) runs it and
fails on any panic, hang, crash or out-of-memory, or when a file in
`conformance/baseline.json` stops reading identically. First report, on
42b81d9: 467 of 697 files identical to h5py, 123 our-error, 15 mismatch
(2 of them an h5py bug), 92 that libhdf5 cannot read, no panics, hangs or
crashes. Compared with the ad-hoc audit sweep, the probe now compares
N-Bit floats (and integers with a bit offset) as the values libhdf5
converts them to rather than raw file bytes — 8 files that were reported as
mismatches read identically — and the reference side no longer flips
between runs when libhdf5 aborts while freeing h5py objects.
- `clawhdf5-format`: **every `f32` dataset was unreadable by h5py and
libhdf5.** The float datatype encoder hard-coded the sign bit's position to
63, correct only for `f64`; libhdf5 validates it and refused the dataset. It
is now derived from the type (15 / 31 / 63). Our reader ignores the field,
and the interop suites only wrote `f64`, which is how it went unnoticed.
- `clawhdf5-format`: **every empty dataset was unreadable by h5py and
libhdf5.** It was written with a real address and zero bytes, which trips
libhdf5's `addr + size <= addr` overflow check. An empty contiguous dataset
now gets the undefined address, as libhdf5 writes it. This affected every
agent store without sessions or a knowledge graph.
- New interop tests: `f32` and `float16` datasets in both directions (our
`float16` rounding matches numpy's bit for bit on 4 020 probe values,
including ties, subnormals and the overflow boundary), and an agent store —
`f32` and `float16` — opened by h5py with every dataset decoded.
- `clawhdf5-format` filters, checked against libhdf5 + hdf5plugin:
- **LZ4 (32004) now uses the registered HDF5 LZ4 format** (8-byte BE size,
4-byte BE block size, BE-length-prefixed blocks). Our old framing (4-byte
LE size + one block) was readable only by clawhdf5, and we could not read
libhdf5's (`h5ex_d_lz4.h5`). Old clawhdf5 LZ4 chunks still read; they are
told apart unambiguously (a registered chunk starts with four zero bytes).
- **Zstd (32015) frames now record the content size**, which libhdf5's zstd
plugin needs; h5py could not read our zstd datasets.
- **Pcodec moved from filter ID 32023 to 480.** 32023 is registered to
Granular BitRound, whose decode is a pass-through — libhdf5 with that
plugin would have returned compressed bytes as data. Pcodec has no
registered ID; 480 is in the registry's private range (256–511) and only
clawhdf5 can read it. Chunks written under 32023 with the filter name
`pcodec` (clawhdf5 ≤ 2.7.0) still read.
- **SZIP decode matches libhdf5.** It returned garbage or zeros with no
error for libhdf5-written files (the 4-byte size prefix, 32/64-bit
byte-plane interleaving, reference interval, scanline padding and byte
order were all handled wrongly) and rejected 64-bit data.
- N-Bit honours libhdf5's "need not compress" flag (multi-filter pipelines
such as `tfilters.h5` failed) and reads enum/no-op members.
- Scale-offset `float` decode uses libhdf5's single-precision arithmetic
(was 1 ULP off for some values).
- A pipeline with Fletcher32 ahead of the compressor (h5py
`set_fletcher32()` then `set_deflate()`) no longer fails with "deflate:
output exceeds size limit".
- `clawhdf5-format`: **HDF5 1.4/1.6-era files are readable.** Data Layout
message versions 1 and 2 (compact, contiguous, and chunked through the
version-1 B-tree) failed with `InvalidLayoutVersion` — 84 of the 686 files in
the 2026-09-25 audit sweep, 205 datasets. They now read as libhdf5 does;
checked byte for byte against h5py on HDF5's own test files
(`tests/legacy_format_interop.rs`).
### Storage
- `clawhdf5-format`: **half-precision datasets.**
`DatasetBuilder::with_f16_data` writes IEEE binary16 (numpy `float16`),
rounding to nearest-even; `make_f16_type`, and `clawhdf5_format::float16`
with the conversions, which are checked against the `half` crate on 16.7M
values and round-trip all 65 536 half values. Reading `float16` as `f32`
gained a little-endian fast path.
- `clawhdf5-agent`: **`MemoryConfig::float16` stores embeddings as half
precision.** At 100K x 384 the file goes from 154.0 to 80.8 MiB (−48%), a
checkpoint from 752 to 512 ms and open from 300 to 252 ms, with the same
vector recall@10 against an exact scan (0.999 vs 0.994) and the same
`hybrid_search` latency; at 10K open is 3 ms slower. On the full
LongMemEval haystack with real MiniLM embeddings every retrieval metric is
identical to `f32` (`longmemeval_bench --float16`). The cache rounds each
embedding as it is saved, so memory and file agree bit for bit and a store
returns the same results before and after a reopen (tested). Out-of-range
values are refused with `MemoryError::InvalidEntry` rather than stored as
infinity; batches are all or nothing. CLI: `create --float16`. See
`BENCHMARKS.md`, "float16 embedding storage".
### Build
- **Pure-Rust default.** `clawhdf5-format`, `clawhdf5-filters` and the
`clawhdf5` facade default to the `zlib-rs` deflate backend; `fast-deflate`
(zlib-ng) is opt-in. No crate in the default dependency tree of the core
crates compiles C, and `ci-test.sh` now fails if one appears. The facade's
`fast-deflate` was on by default and is now off. See `BENCHMARKS.md`,
"Deflate backend".
- `zlib-rs` also enables flate2's `runtime_detection`. Without it zlib-rs has
no `std`, cannot detect SIMD at runtime, and inflates 3.5x slower; the
workspace builds flate2 with `default-features = false`, which had been
switching it off.
- `rust-version = "1.92"` for the whole workspace (the floor: `wgpu` requires
it), and CI checks the workspace on exactly that toolchain.
- CI keeps zlib-ng building and tested; the arm64 job no longer needs cmake.
### Correctness
- `clawhdf5-format` VDS: variable-length and reference data from a source in
another file is refused. Those elements are global-heap IDs and object
addresses in the source file; copied into the virtual dataset they would
be decoded against the wrong file and name another object.
- `clawhdf5-agent`: a store whose `/meta` has an attribute that cannot be
decoded fails to open (`MemoryError::Schema`). With `attrs()` now leaving
unreadable attributes out, it would otherwise have opened with defaults in
place of its settings (`float16`, `compression`, the WAL mark, ...).
- `clawhdf5-format` reader: an old-style group whose local heap has a free
list pointing outside the heap was listed with names read from the broken
heap (garbage names on `cve-2021-36977.h5` once its user block was
applied). libhdf5 refuses such a heap ("bad heap free list"); so do we now,
with `FormatError::InvalidLocalHeapFreeList`. As in libhdf5 the free list
is checked when the first name is read (`LocalHeap::validate_free_list`,
new), so an empty group with a damaged heap still lists as empty.
- **Files with a user block** (`h5py.File(..., userblock_size=N)`, `h5jam`;
the superblock at 512, 1024, …) could not be read: every address in the
file is relative to the superblock, but it was applied from byte 0
(`InvalidObjectHeaderVersion` on the root group). `File` (mmap, buffered,
`from_bytes`), `MmapFile`, `LazyFile`, `AsyncHDF5File`, the VOL readers,
the HNSW loader and external VDS sources now view the file from the
superblock on, using the signature's position as the base address as
libhdf5 does; `user_block_size()` reports the user block (h5py's
`userblock_size`), and `as_bytes()` returns the bytes from the superblock
on. **Breaking (format crate):** `Superblock::parse` refuses a non-zero
signature offset with `FormatError::UserBlockNotStripped`, since the
addresses it returns would be applied to the wrong bytes; pass the slice
from `signature::split_user_block` (new) and parse at offset 0.
- `clawhdf5-format` reader: version-1 shared messages (HDF5 1.6-era files,
e.g. a dataset using a committed datatype in libhdf5's `tcompound.h5`)
read the heap-offset field of the embedded symbol-table entry as the
target address and failed with `InvalidObjectHeaderVersion`. The address
is now read after it, as libhdf5 does. **Breaking (format crate):**
`shared_message::parse_shared_ref` takes `length_size`. A reference whose
target header has no message of the referenced type is now
`FormatError::SharedMessageTargetMissing` instead of returning the first
other message found there (which decoded as garbage).
- `clawhdf5-format` reader: array members of version-1 compound datatypes
(HDF5 1.6-era files, e.g. libhdf5's `tcompound.h5`) were read as a single
element: a `[4] i32` member came back as one `i32`, with the wrong size.
The legacy per-member dimension fields are now decoded into an array type,
as libhdf5 does; more than four dimensions, or a zero-sized one, is an
error.
- `clawhdf5-format` virtual datasets (VDS), checked against HDF5 2.0 through
h5py (`crates/clawhdf5/tests/vds_interop.rs`):
- **Wrong data:** elements no mapping supplies — unmapped regions, and
mappings whose source file or dataset is missing — read as 0 instead of
the virtual dataset's fill value (e.g. h5py `fillvalue=-1`). Assembly moved
to the new `vds` module: `vds::read_virtual_dataset` takes the fill value
and a resolver that can refuse a name (`VdsFileResolver`), and `File`
passes the dataset's fill value. A missing source *dataset* read as an
error; it is fill now, as in libhdf5. Source datasets are read with their
own fill value for unallocated chunks, and a source whose datatype differs
from the virtual dataset's is an error (libhdf5 converts; we do not).
`File` now refuses a source name that leaves the virtual file's directory
(`../x.h5`, absolute paths), or any external source of a `File::from_bytes`
file, with an error — these used to read as fill.
**Behaviour change:** the raw-read API (`read_raw_data_full*`), which has
no fill value, now returns an error for a virtual dataset with unmapped
elements instead of zeros.
- Unlimited and printf-style mappings are supported (all 7 VDS files in the
libhdf5 test set are such mappings, e.g. Eiger/Percival detector layouts).
`%b` in a source file or dataset name is the block number and `%%` a
literal `%` (other `%` sequences are an error, as in libhdf5); block `j`
is read from the source named with `j`, probing from 0 up to the first
missing source. Unlimited source/virtual selections cover as much as the
source's current extent fills, including a partial last block. As
libhdf5 does on `H5Dget_space`, the extent is recomputed from the sources
present (default "last available" view, printf gap 0) —
`vds::virtual_dataset_extent`, used by `Dataset::shape()` — so e.g.
`vds-eiger.h5` is `[5, 10, 10]`, not its stored `[20, 10, 10]`. A source
stored in the other byte order is byte-swapped (libhdf5 converts);
other type conversions remain an error.
- Hyperslab selection versions 1 and 2 were refused ("only version-3
hyperslab selections are supported"). Version 1 is what libhdf5 writes for
every VDS created with the default format bounds (h5py's default), so
those could not be read at all; version 2 is its encoding of an unlimited
selection. Both are decoded now, as are irregular hyperslabs (a union of
blocks, read in row-major order as libhdf5 iterates them).
`SerializedSelection` exposes the raw form, including unlimited counts.
- The version-1 mapping list HDF5 2.0 writes (low version bound 2.0) was
misparsed: each entry's flags byte was read as the start of the source
file name, and names shared with an earlier entry (stored as that entry's
index) were not followed. Now decoded as `H5D__virtual_load_layout` does.
- `clawhdf5-format` reader — **values returned wrong with no error:**
- Fixed Array and Extensible Array chunk indexes were laid out by the
dataset's current shape instead of its max shape (23 libhdf5 test files,
and any h5py file with e.g. `maxshape=(10, None)` or `(20, 10)` under
`libver='latest'`).
- Files with 4-byte offsets: unfiltered chunked datasets read as zeros.
Chunk B-tree keys store offsets in 8 bytes whatever the file's offset
size.
- A chunk's filter mask skipped the whole pipeline when any bit was set;
only the flagged filters are skipped now.
- Float data read as an integer returned the bit pattern; narrowing integer
reads kept the low bits; bfloat16 was decoded as IEEE half. Floats are now
decoded from their datatype fields (bf16, FP8 E4M3/E5M2, IEEE half, single
and double).
- `vl_data::read_vl_bytes` truncated sequences of non-byte base types.
- A shared fill-value message read as zero fill; it is resolved now,
including from the file's shared-message (SOHM) table, which could never
resolve because its index version byte was skipped.
- Two threads reading two chunked datasets through one `File` could get each
other's chunks (the shared chunk cache was switched between datasets
across separate lock acquisitions). The cache is now keyed by dataset.
- Compound datatype version 1 members with legacy array dimensions (HDF5
before 1.4, which had no array class) were read as a single scalar at
the member's offset; they are now array members, as in libhdf5
(`tarrold.h5`, `tcompound.h5`). Only reachable once layout versions 1/2
were readable, since the files that use it are that old.
- `clawhdf5-format` reader — errors on valid files: a version-1 shared
message (a committed datatype in HDF5 1.4/1.6-era files) was read as if the
object header address followed the reserved bytes; it follows a link-name
offset (the reference is an old-style symbol table entry), so the reader
followed the name offset and failed with `InvalidObjectHeaderVersion`
(`tcompound.h5`). New `shared_message::parse_shared_ref_sized` takes the
superblock's length size; `parse_shared_ref` assumes it equals the offset
size.
- `clawhdf5-format` reader — errors on valid files: enum and bool datasets
through the numeric readers; the "don't filter partial edge chunks" layout
flag; Fletcher32 ahead of deflate (NetCDF-4's order). Unknown-message flags
follow libhdf5 (`tbogus.h5`): "fail if unknown" is refused, "fail if unknown
and writing" is ignored by a reader.
- `clawhdf5-format` reader — dense groups and attributes (links or
attributes kept in a fractal heap indexed by a v2 B-tree):
- A link heap larger than the root indirect block's direct rows (512 KiB
with libhdf5's defaults: a few thousand long link names, or ~20 000 short
ones) could not be listed: child indirect blocks were given the wrong
number of rows, so every link stored in one was unreachable.
- v2 B-trees of depth 3 or more (a dense group of ~22 000+ links) were
misparsed: internal-node child pointers were read with widths from an
estimate instead of libhdf5's per-depth record capacities, and the
listing failed. The same B-tree code indexes dense attributes, shared
messages and chunks.
- Fractal-heap "huge" objects (larger than the heap's managed-object
limit, 4 KiB by default — e.g. an 8 KiB dense attribute or a link with a
very long name) and "tiny" objects are now read; the ID type was taken
from the wrong bits (6-7, the version, instead of 4-5), so a huge object
failed and took every attribute on its object down with it (NetCDF-4
files such as netcdf4-python's `issue671.nc`). Huge objects are found
directly from the ID or through the huge-object v2 B-tree, filtered or
not.
- Heaps with an I/O filter pipeline (a group created with a filter on its
creation property list compresses its link heap) are now read: the
header's pipeline was skipped with the wrong size, so its checksum was
looked for in the wrong place, and filtered direct blocks were read raw.
- A user-defined link (link class 65-255, e.g. 187 in libhdf5's
`tall.h5`/`tudlink.h5`) made its whole group unlistable. Such links
cannot be followed without the application that registered the class, so
they are now left out of `datasets()`/`groups()` and path lookup, as h5py
leaves out links it cannot open; reserved link types are still an error.
- `clawhdf5` — soft links are listed, as h5py lists them: `datasets()` and
`groups()` on `Group`/`MmapGroup`/`LazyGroup` include each soft link under
its own name as the kind of object it resolves to, and `dataset(name)` /
`group(name)` open through it. Relative targets resolve from the group
holding the link. Dangling or cyclic soft links, external links and
user-defined links are left out (h5py lists their names but cannot open
them). Previously soft links were missing from the listings, and in
old-style (symbol table) groups a soft link made the listing fail. New
`group_v2::resolve_group_children` / `resolve_path_from` and
`group_v1::v1_soft_links` in `clawhdf5-format`.
- `clawhdf5` — one unreadable attribute no longer fails `attrs()` for every
attribute on its object: it is left out of the map, and the new
`attrs_with_errors()` (on every group and dataset handle) returns the map
plus one error per attribute left out. Returned values are always complete.
An error in the attribute index itself (attribute info message, dense heap
header or B-tree) still fails the call. `clawhdf5-format` gains
`attribute::extract_attributes_tolerant`; `extract_attributes_full` stays
strict.
- `clawhdf5-format` reader — files with shared object header messages
(SOHM, `H5Pset_shared_mesg_index`): a datatype, dataspace, filter pipeline
or attribute stored in the file's SOHM heap failed with "invalid shared
message version: 2" — only shared fill values loaded the SOHM table — so
such files' datasets and attributes could not be read.
`shared_message::resolve_shared_message` now loads the table when a
reference needs it (36 cases of the audit's read matrix).
- `clawhdf5-format` writer — **files libhdf5 rejects or reads wrong:**
- Extensible Array (one unlimited dimension): chunks from index 244 on were
written but never indexed and read as 0, by libhdf5 and by us.
- Fixed Array: more than 1 024 chunks gave checksum errors (data blocks
were never paged).
- A finite max shape larger than the shape gave libhdf5 "addr overflow"; an
unlimited dimension that is not the first scrambled the data; several
unlimited dimensions (`(None, None)`) broke the whole file. These now
write the index libhdf5 writes (swizzled Extensible Array, or a B-tree v2
index for several unlimited dimensions).
- Header messages over 64 KiB (the size field is 16 bits) and compact
datasets at 65 534–65 535 bytes produced corrupt files.
- Reference, Opaque, BitField and Time datatypes were written as empty
messages; they now encode as HDF5 2.0 does.
- `with_page_size` wrote a nonexistent superblock version 4; it now writes
the v3 superblock and File Space Info message libhdf5 writes.
- `FillTime` values were rotated on disk (NEVER was written as ALLOC, and so
on). New `DatasetBuilder::with_fill_value`.
- An empty-string attribute got a zero-size datatype, which made every
attribute on the object unreadable in libhdf5.
- `maxshape` equal to the shape no longer forces chunked layout.
- `clawhdf5-format`: **a truncated deflate chunk read back short, with no
error.** The deflate filter used flate2's streaming reader, which returns the
bytes it has when the input runs out before the end-of-stream marker. It now
decodes in one pass into a buffer sized to the chunk and reports a
truncated stream as `DecompressionError`. Same fix in `clawhdf5-filters`,
where output longer than the stated size was also silently cut off; it is
now an error.
### Defaults
- `clawhdf5-agent`: `MemoryConfig::float16` defaults to `true` for new stores,
measured rather than assumed: identical LongMemEval retrieval on real
embeddings, 48% smaller files and faster checkpoints and opens at 100K.
`clawhdf5-cli create --f32` opts out; like `--f32-index`, it only ever
switches the default off.
- `clawhdf5-agent`: `MemoryConfig::quantized_index` defaults to `true` for new
stores. The reason it had been off — that int8 search was slower on ARM —
did not survive measurement (see Corrections). Stores that predate the
setting still load it as `false`, so reopening one never changes how its
index is held; a store written by the v2.5.0 CLI is now a test fixture that
guards exactly that, and the test fails if the load default is changed.
- `clawhdf5-cli`: `create --f32-index` opts out. `create` used to assign
`--quantized-index` straight into the config, which under the new default
would have forced every CLI-created store back to f32 unless the caller
knew to ask; it now only ever switches the default off.
### Performance
- `clawhdf5-agent`: consolidation's novelty scoring (each `add_memory` against
the whole working tier) computes the new record's norm once, takes each
comparison in one vectorised pass instead of three, and splits a working
tier of 4 096+ records across threads — same results, tested against the
old formula. It had made `consolidation_efficiency` stall at 100K; the
complete run now takes 8 min and fills in the 100K cycle row (46.66 ms) and
the memory-reduction table.
- `clawhdf5-bench`: `consolidation_efficiency` no longer prints a record-count
ratio as a "BM25 Speedup" (it was never measured), nor claims cycle time
grows sub-linearly (its own numbers grow slightly faster than linearly).
- `clawhdf5-agent`: **knowledge-graph traversal was 6.5x slower than it
should be.** `bfs_neighbors` and `spreading_activation` built an adjacency
index over the whole graph on every call (1efd82c), so a 2-hop BFS over 1K
entities took 155 µs. The index is now cached on `KnowledgeCache` and
checked against a fingerprint of the graph on each use — one pass over
entity ids and relation endpoints, no allocation — so any change, including
direct edits of its public `Vec`s, still rebuilds it (tested). BFS over 1K
entities: 155.1 -> 23.1 µs; spreading activation over 100: 22.8 -> 10.1 µs.
- `clawhdf5-format`, `clawhdf5-filters`: both deflate paths hand the codec the
whole chunk in one call, into a buffer allocated once, instead of streaming
it through a 32 KiB buffer: about 5% on chunked writes and 10% on zlib-ng's
1 MB inflate.
- `clawhdf5-accel`: **`dot_i8` has aarch64 kernels** — `SDOT` for CPUs with
the ARMv8.2 dot-product extension (Cortex-A76 and later, Neoverse-N1, every
Apple Silicon generation) and plain NEON (`vmull_s8` + `vpadalq_s16`) for
the rest, selected at runtime. `SDOT` is issued through inline assembly,
because the `vdotq_s32` intrinsic is still behind the unstable
`stdarch_neon_dotprod` feature. On a Raspberry Pi 5 at N = 100 000 and
equal recall, the quantised index answers **1.18x the queries per second**
of f32 (7 267 vs 6 164) and builds **2.3x faster** (14 464 vs 33 413 ms).
Both kernels are tested bit-for-bit against scalar on real hardware, each
explicitly — dispatch only ever takes one path on a given CPU, so testing
through it alone would have left the plain-NEON fallback unexercised on any
machine with `SDOT`.
### Corrections
- The v2.7.0 entry for `dot_i8` said `quantized_index` stayed off by default
because "aarch64 falls back to the scalar loop", implying the ~13% search
penalty measured on x86 applied on ARM too. It did not. That figure came
from scalar int8 against hand-written AVX2 f32 kernels on x86, whose
portable baseline is SSE2; on aarch64 NEON is the baseline, and measured on
a Pi 5 the scalar int8 loop already matched f32 for search while building
1.76x faster. The claim was extrapolated rather than measured.
## v2.7.0 (2026-09-20)
### Upgrade Notes
+94 -15
View File
@@ -1,7 +1,7 @@
# clawhdf5
## Purpose
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.
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).
## 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` | Compression filters (gzip, LZ4, Zstd, Blosc) |
| `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-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-accelerated I/O via wgpu (hand-written WGSL compute shaders) |
| `clawhdf5-gpu` | GPU vector distance computation via wgpu (hand-written WGSL compute shaders) — not dataset I/O |
| `clawhdf5-accel` | CPU SIMD acceleration path |
| `clawhdf5-migrate` | Schema migration engine |
| `clawhdf5-migrate` | SQLite → HDF5 agent-memory migration |
| `clawhdf5-android` | Android JNI bindings |
| `clawhdf5-cli` | Command-line interface |
| `clawhdf5-napi` | Node.js native addon bindings |
@@ -27,7 +27,12 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
| `clawhdf5-bench` | Benchmark suite |
## Key Features
- Zero-dependency HDF5 read/write (no libhdf5 C library required)
- 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).
- 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
@@ -39,15 +44,20 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
(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` (off by
default, persisted) stores the index's own copy of the embeddings as `i8`,
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. On AVX2 it is
also 1.63x the QPS and 1.8x the build speed (`clawhdf5_accel::dot_i8`); it
stays off by default only because that kernel is AVX2-only and aarch64 falls
back to scalar. `hybrid_search` keeps one incremental BM25
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
@@ -77,8 +87,51 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
`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::compression` uses deflate by default; enable the agent's
`zstd` feature to compress embeddings with Zstd instead (links libzstd).
- `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
@@ -95,7 +148,7 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
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 batch I/O for large dataset processing
- GPU-accelerated vector distance computation (`clawhdf5-gpu`, wgpu); HDF5 I/O itself is CPU-only
- Python and Node.js bindings for cross-language use
- NetCDF-4 compatibility for scientific data interop
@@ -111,6 +164,24 @@ 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
@@ -125,4 +196,12 @@ python -c "import clawhdf5; print(clawhdf5.__version__)"
```
## Integration
ZeroClaw imports this as a Cargo feature (`clawhdf5` feature flag) to persist agent memory with HNSW vector search for context retrieval.
- **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`).
+300
View File
@@ -0,0 +1,300 @@
# 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.
+3
View File
@@ -23,6 +23,9 @@ resolver = "2"
[workspace.package]
version = "2.7.0"
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"
+438 -158
View File
@@ -3,24 +3,102 @@
**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-1.75%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)
[![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)
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.
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.
> **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`.
```
cargo add clawhdf5 # core HDF5 read/write, no agent layer
cargo add clawhdf5-agent --features agent # + agent memory layer
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
```
> **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?
@@ -33,16 +111,16 @@ 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 (716ns) |
| Multi-modal | Multiple stores | Unified cross-modal search |
| Security | Hope for the best | Provenance tracking + anomaly detection |
| 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 |
| Portability | Config + DB + files | **One `.h5` file. Copy it anywhere.** |
---
## Performance
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).
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.
### HDF5 Core I/O (vs libhdf5 1.14.6)
@@ -58,31 +136,63 @@ 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
| Scale | Flat | IVF (nprobe=10) | IVF-PQ | vs MemX¹ |
|-------|------|-----------------|--------|----------|
| 1K | **54 µs** | — | — | — |
| 10K | 753 µs | **27 µs** | — | — |
| 100K | 11.4 ms | 1.32 ms | **1.19 ms** | ~8–76× (see caveat) |
**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):
> 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).
| 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) |
|-------|------|-----------------|--------|----------|
| 1K | **47.4 µs** | — | — | — |
| 10K | 500.5 µs | **24.8 µs** | — | — |
| 100K | 6.58 ms | 592 µs | **869 µs** | <90 ms |
> 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).
### Agent Memory Operations
| Operation | Latency | Scale |
|-----------|---------|-------|
| 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 |
| 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).
### Chunked Write Throughput (codec comparison)
@@ -97,7 +207,7 @@ 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). The ratio overstates the real advantage by an unquantified margin — order-of-magnitude indication only. 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), so the two columns are not comparable and no ratio is given. See [BENCHMARKS.md](BENCHMARKS.md#comparison-to-memx-arxiv260316171).
### LongMemEval Retrieval Recall
@@ -115,13 +225,17 @@ 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 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).
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).
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
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
number here measured.
On the easier `longmemeval_oracle` variant (evidence sessions only) the same
@@ -146,19 +260,53 @@ retrieval recall reported as QA accuracy typically overstates by 20–30 points.
### Memory Footprint
| 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) |
**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.
### 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 | ~110 | −89% |
| Hit@1 recall | ~60% | ~90% | +30% |
| Search latency | ~2.8 ms | ~0.3 ms | **9x faster** |
| 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.
**Full benchmark details: [BENCHMARKS.md](BENCHMARKS.md)**
@@ -166,74 +314,74 @@ retrieval recall reported as QA accuracy typically overstates by 20–30 points.
## Agent Memory Architecture
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.
ClawhDF5's agent memory engine draws on 15+ recent papers on agentic memory systems (see [Research Foundation](#research-foundation)).
```
┌─────────────────┐
│ Agent Query │
└────────┬────────┘
│
┌────────────▼────────────┐
│ Hybrid Retrieval │
│ Vector + BM25 + RRF │
└────────────┬────────────┘
│
┌──────────────────▼──────────────────┐
│ Multi-Factor Re-Ranking │
│ temporal · authority · activation │
└──────────────────┬──────────────────┘
│
┌────────────▼────────────┐
│ Confidence Rejection │
┌─────────────────▼──────────────────┐
│ 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) │
└────────────┬────────────┘
└─────────────────┬──────────────────┘
│
┌────────────────────────▼────────────────────────┐
│ 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 │
└─────────────────┘
┌────────────────────────────▼────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────┘
```
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 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 |
| **`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` |
| **`temporal`** | Sorted timestamp index, session DAG, entity timeline, temporal query hints |
| **`multimodal`** | Cross-modal search across text/image/audio/video embeddings |
| **`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 |
| **`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) |
| **`vector_search`** | Flat cosine, pre-normed, SIMD, BLAS, GPU, parallel search paths |
| **`ivf` / `pq`** | IVF-PQ approximate nearest neighbor for billion-scale search |
| **`bm25`** | BM25 keyword index with TF-IDF scoring |
| **`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 |
| **`entity_extract`** | Rule-based entity extraction from text chunks into the knowledge graph |
| **`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 |
| **`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 |
| **`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) |
---
@@ -265,7 +413,7 @@ 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", "my-agent", 384);
let config = MemoryConfig::new("agent.h5".into(), "my-agent", 384);
let mut memory = HDF5Memory::create(config)?;
// Save a memory
@@ -278,13 +426,67 @@ memory.save(MemoryEntry {
tags: "preference".into(),
})?;
// Search
let results = memory.search(&query_embedding, 5)?;
// 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);
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
@@ -309,8 +511,8 @@ 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 resolved = kg.resolve_or_create("alice", "person", -1, 2);
// Returns existing Alice entity (Levenshtein distance ≤ 2)
let (id, created) = kg.resolve_or_create("alice", "person", -1, 2);
// id == alice, created == false: matched the existing entity (Levenshtein distance ≤ 2)
```
### Memory Consolidation
@@ -321,15 +523,19 @@ use clawhdf5_agent::consolidation::*;
let config = ConsolidationConfig::default();
let mut engine = ConsolidationEngine::new(config);
// 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);
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);
// Access a memory (reactivates it)
engine.access_memory(0);
engine.access_memory(id, now);
// Run consolidation cycle
let stats = engine.consolidate();
engine.consolidate(now);
let stats = engine.get_stats();
// 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
@@ -351,19 +557,25 @@ let ids = index.range_query(1700000000.0, 1700010800.0);
let recent = index.latest(10);
```
### OpenClaw Integration
### 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`.
```rust
use clawhdf5_agent::openclaw::*;
// Create backend
let mut backend = ClawhdfBackend::create("memory.h5", "agent-1", 384)?;
let mut backend = ClawhdfBackend::create(std::path::Path::new("memory.h5"), 384)?;
// Ingest existing Markdown memory files
let md = std::fs::read_to_string("MEMORY.md")?;
let count = backend.ingest_markdown("MEMORY.md", &md)?;
// Search (uses full pipeline: RRF → re-rank → confidence filter)
// Search (full pipeline: weighted vector + BM25 fusion → re-rank → confidence filter)
let results = backend.search("user preferences", &query_embedding, 5);
// Export back to Markdown
@@ -375,22 +587,23 @@ let exported = backend.export_markdown("MEMORY.md")?;
## Crate Map
```
clawhdf5 workspace (16 crates, ~92K lines of Rust; plus libaec-sys, an
internal FFI bindings crate for the optional szip feature)
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)
│
├── Core HDF5
│ ├── clawhdf5-format — Binary parser/writer (no_std), shared type definitions
│ ├── clawhdf5-io — I/O abstraction (buffered, mmap, async)
│ ├── 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-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 (NEON, AVX2, AVX-512)
│ ├── clawhdf5-accel — SIMD (AVX2, NEON incl. SDOT int8; AVX-512 behind `avx512`)
│ └── clawhdf5-gpu — GPU compute (wgpu, hand-written WGSL compute shaders)
│
├── Agent Memory
│ ├── 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-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-migrate — SQLite → HDF5 migration
│ ├── clawhdf5-android — Android JNI bridge
│ └── clawhdf5-cli — CLI tool
@@ -411,10 +624,10 @@ ClawhDF5's agent memory design draws from 15+ recent papers:
| Paper | Key Insight | ClawhDF5 Module |
|-------|-------------|-----------------|
| **MemX** (2026) | RRF + multi-factor re-ranking | `hybrid`, `reranker` |
| **Graph-Native Cognitive Memory** (2026) | Graph-structured belief revision | `knowledge` |
| **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` |
| **CraniMem** (2026) | Bounded hippocampal memory | `consolidation` |
| **D-MEM** (2026) | Reward prediction error gating | `consolidation` |
| **D-MEM** (2026) | Surprise-gated storage (implemented as a novelty score) | `consolidation` |
| **SYNAPSE** (2025) | Spreading activation for recall | `knowledge` |
| **RAGdb** (2025) | Zero-dependency edge RAG | Architecture |
| **MemoryGraft** (2025) | Memory poisoning attacks | `anomaly`, `provenance` |
@@ -429,30 +642,45 @@ ClawhDF5's agent memory design draws from 15+ recent papers:
| Flag | Default | Description |
|------|---------|-------------|
| `agent` | no | Full agent memory layer |
| `float16` | **yes** | Half-precision embedding storage (2× compression) |
| `float16` | **yes** | Half-precision cosine kernel (`cosine_similarity_f16`). Half-precision *storage* is the `MemoryConfig::float16` setting below, and needs no feature |
| `hnsw` | **yes** | HNSW approximate vector index for `hybrid_search` (via `clawhdf5-ann`); disable for an exact linear scan |
`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` (off by default) stores 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. On AVX2 it is also **faster** — 1.63x the queries per second and 1.8x
the build speed at equal recall — because the int8 kernel is SIMD too. It
stays off by default only because that kernel is AVX2-only and aarch64 falls
back to a scalar loop. See `BENCHMARKS.md`, "Quantising the index copy".
| `parallel` | no | Rayon parallel search |
| `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) |
| `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 |
@@ -461,26 +689,31 @@ back to a scalar loop. See `BENCHMARKS.md`, "Quantising the index copy".
| `deflate` | yes | Deflate compression |
| `checksum` | yes | Jenkins lookup3 verification |
| `provenance` | yes | SHA-256 provenance attributes |
| `fast-deflate` | **yes** | zlib-ng backend for faster deflate |
| `system-zlib-decompress` | **yes** | Use the system zlib for decompression where available |
| `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) |
| `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 (id 32023, via `pco` crate) |
| `system-zlib` / `zlib-rs` | no | Alternative zlib backends for deflate |
| `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) |
| `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 | Rayon-parallel neighbor-distance computation during HNSW graph pruning |
| `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`) |
### `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
@@ -494,17 +727,17 @@ back to a scalar loop. See `BENCHMARKS.md`, "Quantising the index copy".
## Building
```bash
# Default
# Default (pure Rust: no cmake or C compiler needed)
cargo build --workspace
# Agent memory with all accelerations (Linux)
cargo build -p clawhdf5-agent --features "agent,float16,parallel,fast-math"
cargo build -p clawhdf5-agent --features fast-math
# Agent memory with Apple Accelerate (macOS)
cargo build -p clawhdf5-agent --features "agent,float16,accelerate,parallel,gpu"
cargo build -p clawhdf5-agent --features "accelerate,gpu"
# Tests
cargo test --workspace # all 1,650+ tests
cargo test --workspace # all 1,850+ 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
@@ -526,25 +759,42 @@ cargo bench -p clawhdf5-bench # h5bench-equivalent I/O suite
```
agent_memory.h5
├── /meta
│ ├── schema_version: "1.0"
│ ├── agent_id, embedder, embedding_dim
│ └── created_at
├── /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)
├── /memory
│ ├── chunks: string[N]
│ ├── embeddings: f32[N × D] (or f16 with float16 flag)
│ ├── 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]
│ ├── tombstones: u8[N]
│ └── norms: f32[N] (pre-computed L2)
│ ├── norms: f32[N] (pre-computed L2)
│ └── activation_weights: f32[N] (Hebbian)
├── /sessions
│ ├── ids: string[S]
│ └── summaries: string[S]
│ ├── ids, channels, summaries: string[S]
│ ├── start_idxs, end_idxs: i64[S]
│ └── timestamps: f64[S]
└── /knowledge_graph
├── entity_names: string[E]
├── relation_srcs: i64[R]
├── relation_tgts: i64[R]
└── relation_types: string[R]
├── 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)
```
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
@@ -563,9 +813,39 @@ 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 --embedding-dim 384
clawhdf5-migrate --sqlite old.db --hdf5 memory.h5 --agent-id my-agent --embedder minilm
```
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
@@ -579,10 +859,10 @@ 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)
- ✅ OpenClaw integration layer
- ✅ Markdown ingest/export backend (`ClawhdfBackend`); an OpenClaw plugin was never built — see [docs/openclaw.md](docs/openclaw.md)
- ✅ Comprehensive Criterion benchmarks
**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.
**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)).
---
@@ -599,6 +879,6 @@ MIT
---
<p align="center">
<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>
<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>
</p>
+14 -8
View File
@@ -105,24 +105,30 @@
---
## Track 7: OpenClaw Integration
**Status:** 🟢 Complete
## Track 7: OpenClaw Integration — withdrawn (2026-09-25)
**Status:** ⚪ Withdrawn (the items below were library work; no OpenClaw integration shipped)
**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** memory_search tool — backed by full hybrid retrieval pipeline
- [x] **7.5** memory_get tool — get() with path + line range support
- [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] **7.6** Compaction integration — run_compaction() (decay + compact + WAL flush), run_consolidation() (hippocampal engine), tick_session(), flush_wal()
- [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)
- [ ] **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
**Node.js bridge:** `clawhdf5-napi` (napi-rs) → `@redclaw/clawhdf5` npm package with full TypeScript types.
**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).
---
> **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
@@ -142,7 +148,7 @@
**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) + Track 7 (OpenClaw integration)~~ 🟢 Complete
**Phase 3:** ~~Track 6 (multi-modal)~~ 🟢 Complete; Track 7 (OpenClaw integration) withdrawn
**Phase 4:** ~~Track 8 (benchmarking + validation)~~ 🟢 Complete
All 8 tracks delivered. 1,650+ tests passing, zero clippy warnings.
+3
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@@ -0,0 +1,3 @@
/.cache/
# pin the probe's dependencies (the workspace lock is not committed)
!/probe/Cargo.lock
+39
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@@ -0,0 +1,39 @@
# 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.
+618
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@@ -0,0 +1,618 @@
{
"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",
"hdf5/tools/test/testfiles/binin32.h5",
"hdf5/tools/test/testfiles/binin8.h5",
"hdf5/tools/test/testfiles/binin8w.h5",
"hdf5/tools/test/testfiles/binuin16.h5",
"hdf5/tools/test/testfiles/binuin32.h5",
"hdf5/tools/test/testfiles/bounds_latest_latest.h5",
"hdf5/tools/test/testfiles/charsets.h5",
"hdf5/tools/test/testfiles/compounds_array_vlen1.h5",
"hdf5/tools/test/testfiles/compounds_array_vlen2.h5",
"hdf5/tools/test/testfiles/err_attr_dspace.h5",
"hdf5/tools/test/testfiles/file_space.h5",
"hdf5/tools/test/testfiles/filter_fail.h5",
"hdf5/tools/test/testfiles/h5clear_fsm_persist_equal.h5",
"hdf5/tools/test/testfiles/h5clear_fsm_persist_noclose.h5",
"hdf5/tools/test/testfiles/h5clear_fsm_persist_user_equal.h5",
"hdf5/tools/test/testfiles/h5clear_fsm_persist_user_less.h5",
"hdf5/tools/test/testfiles/h5clear_sec2_v0.h5",
"hdf5/tools/test/testfiles/h5clear_sec2_v2.h5",
"hdf5/tools/test/testfiles/h5copy_extlinks_src.h5",
"hdf5/tools/test/testfiles/h5copy_extlinks_trg.h5",
"hdf5/tools/test/testfiles/h5copy_ref.h5",
"hdf5/tools/test/testfiles/h5copytst.h5",
"hdf5/tools/test/testfiles/h5copytst_new.h5",
"hdf5/tools/test/testfiles/h5diff_attr1.h5",
"hdf5/tools/test/testfiles/h5diff_attr2.h5",
"hdf5/tools/test/testfiles/h5diff_attr3.h5",
"hdf5/tools/test/testfiles/h5diff_attr_v_level1.h5",
"hdf5/tools/test/testfiles/h5diff_attr_v_level2.h5",
"hdf5/tools/test/testfiles/h5diff_basic1.h5",
"hdf5/tools/test/testfiles/h5diff_basic2.h5",
"hdf5/tools/test/testfiles/h5diff_comp_vl_strs.h5",
"hdf5/tools/test/testfiles/h5diff_danglelinks1.h5",
"hdf5/tools/test/testfiles/h5diff_danglelinks2.h5",
"hdf5/tools/test/testfiles/h5diff_dset1.h5",
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]
}
+88
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@@ -0,0 +1,88 @@
#!/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())
+289
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@@ -0,0 +1,289 @@
#!/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)]))
+19
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@@ -0,0 +1,19 @@
# 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/*
+39
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@@ -0,0 +1,39 @@
#!/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"
+48
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@@ -0,0 +1,48 @@
#!/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])
+458
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@@ -0,0 +1,458 @@
# This file is automatically @generated by Cargo.
# It is not intended for manual editing.
version = 4
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name = "adler2"
version = "2.0.1"
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version = "0.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ef0a3155e943e341e557863e69a708999c94ede624e37865c8e2a91b94efa78f"
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version = "0.10.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3078c7629b62d3f0439517fa394996acacc5cbc91c5a20d8c658e77abd503a71"
dependencies = [
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]
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name = "byteorder"
version = "1.5.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
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name = "cc"
version = "1.5.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f360145194ee8e21db5ee7f3fcd4fe52210864c75c985dae33218202c8bbe040"
dependencies = [
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name = "cfg-if"
version = "1.0.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4e7648175b45a9a48536d676f68d918270699102aa8dab5496df06904c914600"
[[package]]
name = "clawhdf5-format"
version = "2.7.0"
dependencies = [
"byteorder",
"flate2",
"libaec-sys",
"lz4_flex",
"pco",
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"sha2",
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+25
View File
@@ -0,0 +1,25 @@
[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"
+898
View File
@@ -0,0 +1,898 @@
//! 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());
}
}
+259
View File
@@ -0,0 +1,259 @@
#!/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])
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@@ -0,0 +1,335 @@
#!/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")
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# 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
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#!/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"
+27
View File
@@ -0,0 +1,27 @@
#!/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
+1
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-accel"
version = "2.7.0"
edition = "2024"
rust-version.workspace = true
description = "SIMD-accelerated operations for rustyhdf5"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
+52 -3
View File
@@ -124,11 +124,24 @@ 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. 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.
/// 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).
@@ -760,6 +773,42 @@ mod dot_i8_tests {
}
}
/// 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.
+127
View File
@@ -180,3 +180,130 @@ 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
}
+9 -1
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-agent"
version = "2.7.0"
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"
@@ -18,6 +19,10 @@ 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 }
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 }
@@ -44,6 +49,10 @@ harness = false
name = "memory_bench"
harness = false
[[bench]]
name = "multimodal_bench"
harness = false
[features]
default = ["float16", "hnsw", "parallel"]
float16 = ["half"]
@@ -59,7 +68,6 @@ zstd = ["clawhdf5/zstd"]
# `--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"]
@@ -0,0 +1,107 @@
//! 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);
+99
View File
@@ -1,6 +1,7 @@
//! 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.
///
@@ -149,6 +150,11 @@ 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 {
@@ -164,9 +170,44 @@ 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")]
@@ -202,6 +243,7 @@ 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);
@@ -240,6 +282,7 @@ 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);
@@ -439,4 +482,60 @@ mod tests {
.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));
}
}
+126 -2
View File
@@ -144,9 +144,44 @@ 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.
/// Returns 0.0 if either norm is zero. The reference that
/// [`Self::score_surprise`] is tested against.
#[cfg(test)]
fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
let len = a.len().min(b.len());
if len == 0 {
@@ -167,13 +202,54 @@ 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 {
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(|r| Self::cosine_similarity(embedding, &r.embedding))
.map(similarity)
.fold(f32::NEG_INFINITY, f32::max);
(1.0 - max_sim).clamp(0.0, 1.0)
}
@@ -471,6 +547,54 @@ 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];
+23 -20
View File
@@ -65,31 +65,34 @@ pub fn hybrid_search_fused(
) -> Vec<(usize, f32)> {
// Get raw scores from both systems. Request all results so normalization
// covers the full distribution.
// Use parallel search when rayon feature is enabled and vector count > 10K.
let vec_scores = {
#[cfg(feature = "parallel")]
{
if vectors.count() > 10_000 {
vector_search::parallel_cosine_batch(
query_embedding,
vectors,
tombstones,
vectors.count(),
)
} else {
vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
}
}
#[cfg(not(feature = "parallel"))]
{
vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
}
};
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)> {
#[cfg(feature = "parallel")]
{
if vectors.count() > 10_000 {
return vector_search::parallel_cosine_batch(
query_embedding,
vectors,
skip,
vectors.count(),
);
}
}
vector_search::cosine_similarity_batch(query_embedding, vectors, skip)
}
/// Merge pre-computed vector-similarity and keyword scores into a single ranking.
///
/// Both score sets are independently min-max normalized to [0, 1] and combined
+115 -8
View File
@@ -163,12 +163,13 @@ fn levenshtein(a: &str, b: &str) -> usize {
/// entities-slice-index map, and an entity-id -> relation-indices map (edges
/// touching that entity as either source or target).
///
/// Built fresh per traversal call rather than cached on `KnowledgeCache`:
/// entities/relations are plain `pub` `Vec`s that get pushed to directly
/// (e.g. `schema.rs`'s load path bypasses `add_entity`/`add_relation`), so a
/// persistent index would need extra bookkeeping to avoid drifting stale. A
/// one-off O(V+E) build per call is still a large win over the O(V·E) (BFS)
/// / O(steps·active·E) (spreading activation) scans it replaces.
/// 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>>,
@@ -204,6 +205,45 @@ impl AdjacencyIndex {
}
}
/// 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
// ---------------------------------------------------------------------------
@@ -216,6 +256,7 @@ pub struct KnowledgeCache {
pub alias_strings: Vec<String>,
pub alias_entity_ids: Vec<i64>,
next_entity_id: u64,
adjacency: AdjacencyCache,
}
impl KnowledgeCache {
@@ -226,6 +267,7 @@ impl KnowledgeCache {
alias_strings: Vec::new(),
alias_entity_ids: Vec::new(),
next_entity_id: 0,
adjacency: AdjacencyCache::default(),
}
}
@@ -236,9 +278,29 @@ 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
// -----------------------------------------------------------------------
@@ -397,7 +459,7 @@ 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 = AdjacencyIndex::build(&self.entities, &self.relations);
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();
@@ -502,7 +564,7 @@ impl KnowledgeCache {
min_activation: f32,
max_steps: usize,
) -> Vec<(u64, f32)> {
let idx = AdjacencyIndex::build(&self.entities, &self.relations);
let idx = self.adjacency_index();
let mut activation: HashMap<u64, f32> = HashMap::new();
// Initialise seeds with activation 1.0.
@@ -631,6 +693,51 @@ 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
// -----------------------------------------------------------------------
+260 -9
View File
@@ -1,4 +1,4 @@
//! ZeroClaw agent memory HDF5 backend.
//! Agent memory stored in a single HDF5 file.
//!
//! Provides persistent memory storage for AI agents using HDF5 files.
//! All data is cached in-memory for fast access and flushed to disk
@@ -36,6 +36,7 @@ pub mod reranker;
pub mod schema;
pub mod search;
pub mod session;
pub mod signing;
pub mod storage;
mod store_lock;
pub mod temporal;
@@ -63,6 +64,7 @@ use std::path::{Path, PathBuf};
use cache::MemoryCache;
#[cfg(feature = "hnsw")]
use clawhdf5_ann::{DistanceMetric, HnswIndex, Storage};
use clawhdf5_format::float16::round_to_f16;
use ephemeral::{EphemeralConfig, EphemeralStore};
// EphemeralEntry and EphemeralStats are part of the crate public API via
@@ -71,11 +73,13 @@ use ephemeral::{EphemeralConfig, EphemeralStore};
pub use ephemeral::{EphemeralEntry, EphemeralStats};
use knowledge::KnowledgeCache;
use memory_strategy::{Exchange, MemoryStrategy, StrategyOutput};
use session::SessionCache;
pub use search::SearchOptions;
pub use session::{SessionCache, SessionEntry};
// --- Error type ---
#[derive(Debug)]
#[non_exhaustive]
pub enum MemoryError {
Io(std::io::Error),
Hdf5(String),
@@ -83,6 +87,14 @@ pub enum MemoryError {
NotFound(String),
/// Another `HDF5Memory` (in this or another process) has the store open.
Locked(String),
/// A record the store cannot hold as given, e.g. an embedding value
/// outside the half-precision range of a `float16` store.
InvalidEntry(String),
/// The store's checkpoints are signed and no signing key is set, so a
/// checkpoint would leave it unsigned. Set the key with
/// [`HDF5Memory::set_signing_key`], or drop the signature on purpose with
/// [`HDF5Memory::remove_signature`].
SigningKeyRequired(String),
}
impl std::fmt::Display for MemoryError {
@@ -93,6 +105,8 @@ impl std::fmt::Display for MemoryError {
MemoryError::Schema(e) => write!(f, "schema error: {e}"),
MemoryError::NotFound(e) => write!(f, "not found: {e}"),
MemoryError::Locked(e) => write!(f, "store is locked: {e}"),
MemoryError::InvalidEntry(e) => write!(f, "invalid entry: {e}"),
MemoryError::SigningKeyRequired(e) => write!(f, "signing key required: {e}"),
}
}
}
@@ -124,6 +138,19 @@ pub struct MemoryConfig {
pub embedding_dim: usize,
pub chunk_size: usize,
pub overlap: usize,
/// Store embeddings as IEEE half precision (numpy `float16`): half the
/// bytes of the embeddings dataset on disk. Every embedding is rounded to
/// the nearest half as it enters the store, in memory as well as on disk,
/// so search results are the same before and after a reopen. Values must
/// lie within ±65504; a save outside that is `MemoryError::InvalidEntry`.
/// Fixed when the store is created (persisted in `/meta`).
///
/// **On by default for new stores**: on the full LongMemEval haystack with
/// real MiniLM embeddings every retrieval metric matched `f32`, and at
/// 100K records the file is 48% smaller (`BENCHMARKS.md`). Existing
/// stores keep the setting they were created with. Set it to `false` for
/// full-precision embeddings, e.g. for unnormalised vectors that may
/// exceed the half-precision range.
pub float16: bool,
pub compression: bool,
pub compression_level: u32,
@@ -134,13 +161,19 @@ pub struct MemoryConfig {
pub wal_enabled: bool,
pub wal_max_entries: usize,
/// Store the vector index's own copy of the embeddings as int8 rather than
/// f32, a quarter of the memory.
/// f32, a quarter of the memory. **On by default** for new stores.
///
/// The index's copy is the single largest part of a loaded store's
/// footprint. Quantised distances are approximate, so the candidate pool
/// is re-scored against the cache's exact embeddings before fusion, which
/// restores recall; what it costs is throughput — roughly 13% of queries
/// per second and 16% of build time at 100K x 384. See `BENCHMARKS.md`.
/// holds recall at the f32 index's level. It is also faster, not slower:
/// at equal recall, 1.63x the queries per second on x86-64 (AVX2) and
/// 1.18x on a Raspberry Pi 5 (NEON `SDOT`), with builds 1.8x and 2.3x
/// faster. See `BENCHMARKS.md`.
///
/// Persisted with the store. Stores written before this setting existed
/// have no stored value and open as `false`, so reopening an old store
/// never changes how its index is held.
///
/// Has no effect without the `hnsw` feature.
pub quantized_index: bool,
@@ -173,7 +206,7 @@ impl MemoryConfig {
embedding_dim,
chunk_size: 512,
overlap: 50,
float16: false,
float16: true,
compression: false,
compression_level: 0,
compact_threshold: 0.3,
@@ -182,7 +215,7 @@ impl MemoryConfig {
created_at,
wal_enabled: true,
wal_max_entries: 500,
quantized_index: false,
quantized_index: true,
hnsw_m: 16,
hnsw_ef_construction: 64,
hnsw_ef_search: 0,
@@ -292,6 +325,12 @@ pub struct HDF5Memory {
activations_dirty: bool,
/// Opened with [`HDF5Memory::open_read_only`]: nothing may reach the disk.
read_only: bool,
/// Key that signs every checkpoint; never persisted. See
/// [`HDF5Memory::set_signing_key`].
signing_key: Option<signing::SigningKey>,
/// Checkpoints of this store are signed: the file on disk is, or a key
/// has been set. A checkpoint without a key is then refused.
signed: bool,
/// A WAL that `open()` could not read and moved aside; see
/// [`HDF5Memory::quarantined_wal`].
quarantined_wal: Option<PathBuf>,
@@ -311,7 +350,8 @@ impl HDF5Memory {
/// Create a new HDF5 memory file with the given configuration.
pub fn create(config: MemoryConfig) -> Result<Self> {
let lock = store_lock::StoreLock::acquire(&config.path)?;
let cache = MemoryCache::new(config.embedding_dim);
let mut cache = MemoryCache::new(config.embedding_dim);
cache.set_half_precision(config.float16);
let sessions = SessionCache::new();
let knowledge = KnowledgeCache::new();
@@ -346,6 +386,8 @@ impl HDF5Memory {
bm25_filter: bm25::TokenFilter::default(),
activations_dirty: false,
read_only: false,
signing_key: None,
signed: false,
quarantined_wal: None,
_lock: Some(lock),
})
@@ -524,6 +566,8 @@ impl HDF5Memory {
bm25_filter: bm25::TokenFilter::default(),
activations_dirty: false,
read_only,
signing_key: None,
signed: checkpoint.signed,
quarantined_wal,
_lock: lock,
})
@@ -684,6 +728,39 @@ impl HDF5Memory {
}
}
/// Sign every checkpoint from now on with `key` (Ed25519). The key is
/// never written anywhere; set it again after every `open`. Once a store
/// is signed, a checkpoint without the key is refused
/// ([`MemoryError::SigningKeyRequired`]) rather than silently leaving it
/// unsigned. Setting a different key re-signs the store under that key
/// from the next checkpoint; a verifier trusting the old key will then
/// reject it, which is the point. Call [`AgentMemory::flush_wal`] to sign
/// right away.
pub fn set_signing_key(&mut self, key: signing::SigningKey) {
self.signing_key = Some(key);
self.signed = true;
}
/// Stop signing: the next checkpoint writes the store unsigned. The
/// deliberate way out of [`MemoryError::SigningKeyRequired`].
pub fn remove_signature(&mut self) {
self.signing_key = None;
self.signed = false;
}
/// Checkpoints of this store are signed (on disk, or from the next
/// checkpoint because a key has been set).
pub fn is_signed(&self) -> bool {
self.signed
}
/// Check the checkpoint at `path` against the public key the caller
/// trusts; see [`signing::verify_store`]. Reads the file only: it works
/// on a store another process has open.
pub fn verify(path: &Path, trusted: &signing::VerifyingKey) -> Result<signing::VerifyReport> {
signing::verify_store(path, trusted)
}
/// Flush current state to disk and truncate the WAL.
///
/// Every code path that persists the full cache to the .h5 file must
@@ -699,10 +776,28 @@ impl HDF5Memory {
// Record which WAL prefix this checkpoint contains, so a crash before
// the truncate below can't replay those entries a second time.
let wal_applied = self.wal.as_ref().map(|w| w.mark());
let signature = match &self.signing_key {
Some(key) => Some(signing::sign(
key,
&self.config,
&self.cache,
&self.sessions,
&self.knowledge,
wal_applied,
)),
None if self.signed => {
return Err(MemoryError::SigningKeyRequired(format!(
"{} is signed; set its signing key before a checkpoint \
(saves so far are held in the WAL or in memory)",
self.config.path.display()
)));
}
None => None,
};
// Written before the .h5 so a crash in between leaves a sidecar whose
// generation matches no checkpoint (ignored), never the reverse.
let ann_generation = self.persist_vector_index();
storage::write_to_disk_with_meta(
storage::write_to_disk_signed(
&self.config.path,
&self.config,
&self.cache,
@@ -711,7 +806,9 @@ impl HDF5Memory {
&schema::CheckpointMeta {
wal_applied,
ann_generation,
signed: signature.is_some(),
},
signature.as_ref(),
)?;
if let Some(ref mut w) = self.wal {
w.truncate()?;
@@ -1002,6 +1099,18 @@ impl HDF5Memory {
&self.config
}
/// The sessions recorded in this store.
pub fn sessions(&self) -> &SessionCache {
&self.sessions
}
/// Mutable access to the sessions, e.g. to add many at once. Changes
/// reach the disk at the next checkpoint (any flushing call, such as
/// [`HDF5Memory::flush_wal`] or `save_batch`), not immediately.
pub fn sessions_mut(&mut self) -> &mut SessionCache {
&mut self.sessions
}
/// Get a reference to the knowledge cache.
pub fn knowledge(&self) -> &KnowledgeCache {
&self.knowledge
@@ -1064,7 +1173,29 @@ impl HDF5Memory {
/// Upsert: if an active entry with the same tags (key) exists, update it in-place.
/// Otherwise append a new entry. Use this for key-based memory stores where
/// the same key should not create duplicates.
/// A `float16` store holds embeddings as IEEE half precision, which has no
/// finite value beyond ±65504. Refuse such an embedding rather than
/// silently store infinity. (Values that are already infinite or NaN are
/// stored as they are, as in an `f32` store.)
fn check_embedding(&self, embedding: &[f32]) -> Result<()> {
if !self.config.float16 {
return Ok(());
}
let overflow = embedding
.iter()
.enumerate()
.find(|&(_, &v)| v.is_finite() && round_to_f16(v).is_infinite());
match overflow {
None => Ok(()),
Some((i, v)) => Err(MemoryError::InvalidEntry(format!(
"embedding[{i}] = {v} is outside the half-precision range (±65504) \
of this float16 store"
))),
}
}
pub fn save_or_update(&mut self, entry: MemoryEntry) -> Result<usize> {
self.check_embedding(&entry.embedding)?;
if let Some(existing_idx) = self.cache.find_by_tags(&entry.tags) {
if let Some(ref mut w) = self.wal {
let wal_entry = wal::WalEntry {
@@ -1120,6 +1251,7 @@ impl HDF5Memory {
impl AgentMemory for HDF5Memory {
fn save(&mut self, entry: MemoryEntry) -> Result<usize> {
self.check_embedding(&entry.embedding)?;
if let Some(ref mut w) = self.wal {
let wal_entry = wal::WalEntry {
entry_type: wal::WalEntryType::Save,
@@ -1161,6 +1293,10 @@ impl AgentMemory for HDF5Memory {
}
fn save_batch(&mut self, entries: Vec<MemoryEntry>) -> Result<Vec<usize>> {
// All or nothing: check every entry before storing any.
for entry in &entries {
self.check_embedding(&entry.embedding)?;
}
let mut indices = Vec::with_capacity(entries.len());
for entry in entries {
let idx = self.cache.push(
@@ -1321,6 +1457,9 @@ impl HDF5Memory {
})?;
let view = memory_strategy::CacheStoreView::new(&self.cache, &self.knowledge);
let output = strat.evaluate(&exchange, &view);
for e in &output.entries {
self.check_embedding(&e.embedding)?;
}
for e in &output.entries {
self.cache.push(
e.chunk.clone(),
@@ -1345,6 +1484,35 @@ impl HDF5Memory {
}
impl HDF5Memory {
/// Delete many records with a single checkpoint, where
/// [`AgentMemory::delete`] checkpoints once per record.
///
/// All or nothing: if any id is out of range or already deleted (or
/// repeated), nothing is deleted and `MemoryError::NotFound` is returned.
/// Unlike `delete`, this never auto-compacts, so the records stay in the
/// store as tombstones (their indices unchanged) until [`AgentMemory::compact`]
/// is called — importers use it to carry over records that were already
/// deleted in the source.
pub fn delete_batch(&mut self, ids: &[usize]) -> Result<()> {
let mut seen = std::collections::HashSet::with_capacity(ids.len());
for &id in ids {
if self.cache.tombstones.get(id).copied() != Some(0) || !seen.insert(id) {
return Err(MemoryError::NotFound(format!(
"entry {id} not found or already deleted"
)));
}
}
if ids.is_empty() {
return Ok(());
}
for &id in ids {
self.cache.mark_deleted(id);
self.hnsw_on_delete(id);
self.bm25_on_delete(id);
}
self.flush()
}
pub fn tick_session(&mut self) -> Result<()> {
let d = self.config.decay_factor;
for w in self.cache.activation_weights.iter_mut() {
@@ -1405,6 +1573,16 @@ impl HDF5Memory {
let mut promoted = 0;
for key in candidates {
// Check before taking, so a rejected entry stays in the ephemeral
// tier rather than being lost.
if let Some(emb) = self
.ephemeral
.as_ref()
.and_then(|s| s.get_entry(&key))
.and_then(|e| e.embedding.as_deref())
{
self.check_embedding(emb)?;
}
let entry = match self
.ephemeral
.as_mut()
@@ -1533,6 +1711,79 @@ mod tests {
}
}
#[test]
fn delete_batch_tombstones_without_compacting() {
let dir = TempDir::new().unwrap();
let path = dir.path().join("test.h5");
let mut mem = HDF5Memory::create(make_config(&dir)).unwrap();
mem.save_batch(
(0..4)
.map(|i| make_entry(&format!("record {i}"), &[i as f32, 1.0, 0.0, 0.0]))
.collect(),
)
.unwrap();
// 3 of 4 is far past compact_threshold (0.3): delete() would compact.
mem.delete_batch(&[0, 1, 3]).unwrap();
assert_eq!(mem.count(), 4);
assert_eq!(mem.count_active(), 1);
drop(mem);
let mut mem = HDF5Memory::open(&path).unwrap();
assert_eq!(mem.cache.tombstones, vec![1, 1, 0, 1]);
let hits = mem.hybrid_search(&[0.0, 1.0, 0.0, 0.0], "record", 0.5, 0.5, 10);
assert!(
hits.iter().all(|r| r.index == 2),
"tombstoned record returned"
);
}
#[test]
fn delete_batch_is_all_or_nothing() {
let dir = TempDir::new().unwrap();
let mut mem = HDF5Memory::create(make_config(&dir)).unwrap();
mem.save_batch(vec![
make_entry("a", &[1.0, 0.0, 0.0, 0.0]),
make_entry("b", &[0.0, 1.0, 0.0, 0.0]),
])
.unwrap();
for bad in [&[0, 5][..], &[1, 1][..]] {
assert!(matches!(
mem.delete_batch(bad),
Err(MemoryError::NotFound(_))
));
assert_eq!(mem.count_active(), 2, "{bad:?} deleted something");
}
mem.delete_batch(&[]).unwrap();
assert_eq!(mem.count_active(), 2);
}
#[test]
fn sessions_mut_add_at_keeps_timestamp_across_reopen() {
let dir = TempDir::new().unwrap();
let path = dir.path().join("test.h5");
let mut mem = HDF5Memory::create(make_config(&dir)).unwrap();
mem.sessions_mut()
.add_at("s-old", 2, 7, "discord", "old summary", 1.7e15);
mem.flush_wal().unwrap();
drop(mem);
let mem = HDF5Memory::open_read_only(&path).unwrap();
let s = mem.sessions();
assert_eq!(s.len(), 1);
let e = &s.entries[0];
assert_eq!(
(
e.id.as_str(),
e.start_idx,
e.end_idx,
e.channel.as_str(),
e.ts
),
("s-old", 2, 7, "discord", 1.7e15)
);
assert_eq!(s.summaries[0], "old summary");
}
#[test]
fn create_new_file() {
let dir = TempDir::new().unwrap();
+25 -67
View File
@@ -1,10 +1,12 @@
//! OpenClaw Integration Layer.
//! A Markdown-oriented memory backend over [`crate::HDF5Memory`].
//!
//! Bridge between OpenClaw agent gateway (Markdown + sqlite-vec) and the
//! clawhdf5 HDF5-backed memory backend. Provides:
//! 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:
//!
//! - [`MemoryBackend`] — the trait OpenClaw implements against.
//! - [`ClawhdfBackend`] — concrete HDF5-backed implementation.
//! - [`MemoryBackend`] — search / read back / write / ingest / export.
//! - [`ClawhdfBackend`] — the HDF5-backed implementation.
//! - [`MarkdownParser`] — splits Markdown into [`MarkdownSection`] records.
//! - [`MarkdownExporter`] — renders sections back to Markdown text.
@@ -13,9 +15,8 @@ use std::path::{Path, PathBuf};
use std::time::{SystemTime, UNIX_EPOCH};
use crate::{
AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry,
confidence::{ConfidenceConfig, ScoredResult, reject_low_confidence},
reranker::{ReRankConfig, RerankInput, rerank},
AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, SearchOptions,
confidence::ConfidenceConfig, reranker::ReRankConfig,
};
// ─────────────────────────────────────────────────────────────────────────────
@@ -62,7 +63,8 @@ pub struct BackendStats {
// MemoryBackend trait
// ─────────────────────────────────────────────────────────────────────────────
/// Interface that OpenClaw uses to interact with a memory backend.
/// A Markdown-oriented memory backend: search, read back by path, write,
/// ingest and export.
///
/// Implementors provide persistent storage, full-text + vector search,
/// Markdown ingestion / export, and statistics.
@@ -319,7 +321,7 @@ impl MarkdownExporter {
///
/// # Path mapping
///
/// OpenClaw addresses memories by file path (e.g. `"memory/user.md"`).
/// Memories are addressed 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>"`.
@@ -422,7 +424,7 @@ impl ClawhdfBackend {
// ── Compaction & Consolidation hooks (7.6) ────────────────────────────
/// Run a compaction cycle — called by OpenClaw during session compaction.
/// Run a compaction cycle (decay, compaction, WAL flush).
///
/// Sequence:
/// 1. `tick_session()` — apply Hebbian decay to all activation weights.
@@ -524,71 +526,27 @@ impl ClawhdfBackend {
impl MemoryBackend for ClawhdfBackend {
/// Search using hybrid vector + BM25 retrieval, then re-rank and
/// confidence-filter.
/// confidence-filter — [`HDF5Memory::search`] with both stages on.
fn search(
&mut self,
query_text: &str,
query_embedding: &[f32],
k: usize,
) -> Vec<MemorySearchResult> {
// 1. Hybrid retrieval (vector + BM25, fused by score).
let candidates = k.saturating_mul(3).max(10);
let raw = self.memory.hybrid_search_with(
query_embedding,
query_text,
crate::hybrid::DEFAULT_FUSION,
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,
relevance: r.score,
})
.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
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)
.into_iter()
.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(),
.map(|r| MemorySearchResult {
text: r.chunk,
score: r.score,
path: r.source_channel.clone(),
line_range: None,
timestamp: Some(r.timestamp),
source: path,
})
source: r.source_channel,
})
.collect()
}
+1 -1
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 (see [`fnv1a_64`]) — this is not a tamper-evidence or
//! The hash is unkeyed (FNV-1a) — this is not a tamper-evidence or
//! authenticity guarantee.
use std::collections::HashMap;
+171 -18
View File
@@ -15,6 +15,9 @@ 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
@@ -23,6 +26,7 @@ pub const ZEROCLAW_VERSION: &str = "0.8.0";
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(
@@ -46,7 +50,7 @@ pub fn build_hdf5_file_with_mark(
) -> Result<Vec<u8>, MemoryError> {
let meta = CheckpointMeta {
wal_applied,
ann_generation: None,
..CheckpointMeta::default()
};
build_hdf5_file_with_meta(config, cache, sessions, knowledge, &meta)
}
@@ -61,6 +65,10 @@ pub struct CheckpointMeta {
/// 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.
@@ -70,6 +78,19 @@ pub fn build_hdf5_file_with_meta(
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();
@@ -130,11 +151,42 @@ pub fn build_hdf5_file_with_meta(
// 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)?;
@@ -159,20 +211,27 @@ fn build_memory_group(
// chunks: fixed-length string array
write_string_dataset(&mut group, "chunks", &cache.chunks);
// embeddings: f32 [N x D]
// 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.
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")
.with_f32_data(flat)
.with_shape(&[n, d]);
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]);
// 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 * 4)).max(1).min(n);
let rows_per_chunk = (target_chunk_bytes / (d * elem_bytes)).max(1).min(n);
ds.with_chunks(&[rows_per_chunk, d]);
// Compression. Shuffle is applied automatically (auto-shuffle
@@ -418,10 +477,34 @@ 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 = file.group("meta").ok()?.attrs().ok()?;
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,
@@ -433,19 +516,75 @@ pub fn read_wal_mark(file: &clawhdf5::File) -> Option<WalMark> {
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 = file
.group("meta")
let ann_generation =
meta_attrs(file)
.ok()
.and_then(|g| g.attrs().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,
}
}
@@ -453,12 +592,7 @@ pub fn validate_and_load(
file: &clawhdf5::File,
) -> Result<(MemoryConfig, MemoryCache, SessionCache, KnowledgeCache), MemoryError> {
// Read /meta group attributes
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 attrs = meta_attrs(file)?;
let schema_version = match attrs.get("schema_version") {
Some(AttrValue::String(s)) => s.clone(),
@@ -497,6 +631,9 @@ pub fn validate_and_load(
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())
@@ -510,7 +647,16 @@ pub fn validate_and_load(
};
// Load /memory group
let memory_cache = load_memory_group(file, embedding_dim)?;
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);
}
// Load /sessions group
let session_cache = load_sessions_group(file)?;
@@ -753,6 +899,13 @@ 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)
+274 -24
View File
@@ -2,18 +2,107 @@
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)
}
}
impl HDF5Memory {
/// Vector + keyword scoring stage of [`HDF5Memory::hybrid_search`].
/// Vector + keyword scoring stage of [`HDF5Memory::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,
@@ -22,16 +111,30 @@ impl HDF5Memory {
bm25: &bm25::BM25Index,
fusion: hybrid::Fusion,
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() => {
// Over-fetch so the merge sees a useful vector pool; cosine
// distance from the index converts back to similarity (1 - d).
// `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 pool = (k * 8).max(64);
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
@@ -42,6 +145,7 @@ impl HDF5Memory {
let exact = index.storage() == clawhdf5_ann::Storage::Int8;
let vec_scores: Vec<(usize, f32)> = candidates
.into_iter()
.filter(|(id, _)| exclude.is_none_or(|ex| ex[*id] == 0))
.map(|(id, dist)| {
let score = if exact {
crate::vector_search::cosine_similarity(
@@ -56,10 +160,54 @@ impl HDF5Memory {
.collect();
// Fusion normalises over every keyword match, so it needs all
// the scores — but not ranked.
let kw_scores = bm25.scores(query_text);
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,
k,
ex,
);
}
kw_scores.retain(|(id, _)| ex[*id] == 0);
}
hybrid::fuse(vec_scores, kw_scores, fusion, k)
}
_ => hybrid::hybrid_search_fused(
_ => match exclude {
Some(ex) => {
self.exact_masked_search(query_embedding, query_text, bm25, fusion, k, ex)
}
None => hybrid::hybrid_search_fused(
query_embedding,
query_text,
&self.cache.embeddings,
&self.cache.chunks,
&self.cache.tombstones,
bm25,
fusion,
k,
),
},
}
}
#[cfg(not(feature = "hnsw"))]
fn vector_keyword_search(
&mut self,
query_embedding: &[f32],
query_text: &str,
bm25: &bm25::BM25Index,
fusion: hybrid::Fusion,
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(
query_embedding,
query_text,
&self.cache.embeddings,
@@ -72,25 +220,33 @@ impl HDF5Memory {
}
}
#[cfg(not(feature = "hnsw"))]
fn vector_keyword_search(
&mut self,
/// 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)> {
hybrid::hybrid_search_fused(
query_embedding,
query_text,
&self.cache.embeddings,
&self.cache.chunks,
&self.cache.tombstones,
bm25,
fusion,
k,
)
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.
@@ -125,12 +281,53 @@ impl HDF5Memory {
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 scored = self.vector_keyword_search(query_embedding, query_text, &bm25, fusion, k);
let scored = self.vector_keyword_search(
query_embedding,
query_text,
&bm25,
options.fusion,
fetch,
exclude.as_deref(),
);
self.bm25 = Some(bm25);
let mut results: Vec<SearchResult> = scored
.into_iter()
.map(|(idx, score)| {
@@ -154,6 +351,25 @@ impl HDF5Memory {
.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
@@ -164,11 +380,45 @@ impl HDF5Memory {
.map(|r| r.index)
.collect();
self.apply_hebbian_boost(&hit_indices);
self.bm25 = Some(bm25);
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
+16 -1
View File
@@ -33,7 +33,7 @@ impl SessionCache {
self.entries.is_empty()
}
/// Add a new session with its summary.
/// Add a new session with its summary, timestamped now.
pub fn add(
&mut self,
id: &str,
@@ -47,6 +47,21 @@ 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,
+419
View File
@@ -0,0 +1,419 @@
//! 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)
}
+16 -2
View File
@@ -36,7 +36,7 @@ pub fn write_to_disk_with_mark(
) -> Result<(), MemoryError> {
let meta = schema::CheckpointMeta {
wal_applied,
ann_generation: None,
..schema::CheckpointMeta::default()
};
write_to_disk_with_meta(path, config, cache, sessions, knowledge, &meta)
}
@@ -50,7 +50,21 @@ pub fn write_to_disk_with_meta(
knowledge: &KnowledgeCache,
checkpoint: &schema::CheckpointMeta,
) -> Result<(), MemoryError> {
let bytes = schema::build_hdf5_file_with_meta(config, cache, sessions, knowledge, checkpoint)?;
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)?;
if bytes.is_empty() {
return Err(MemoryError::Hdf5("build_hdf5_file produced 0 bytes".into()));
Binary file not shown.
@@ -0,0 +1,300 @@
//! `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"),
}
}
+155
View File
@@ -0,0 +1,155 @@
//! 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]);
}
@@ -284,3 +284,63 @@ fn degenerate_hnsw_parameters_do_not_panic() {
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);
}
@@ -0,0 +1,344 @@
//! `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);
}
}
+330
View File
@@ -0,0 +1,330 @@
//! 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<_>>()
);
}
+1
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-android"
version = "2.7.0"
edition = "2024"
rust-version.workspace = true
description = "Android JNI bridge for edgehdf5-memory HDF5 backend"
license = "MIT"
+1
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-ann"
version = "2.7.0"
edition = "2024"
rust-version.workspace = true
description = "HNSW approximate nearest neighbor index stored as HDF5"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
+4 -3
View File
@@ -13,7 +13,7 @@ use clawhdf5_format::filter_pipeline::FilterPipeline;
use clawhdf5_format::group_v2::resolve_path_any;
use clawhdf5_format::message_type::MessageType;
use clawhdf5_format::object_header::ObjectHeader;
use clawhdf5_format::signature::find_signature;
use clawhdf5_format::signature::split_user_block;
use clawhdf5_format::superblock::Superblock;
use clawhdf5_io::FileWriter as IoFileWriter;
@@ -861,8 +861,9 @@ impl HnswIndex {
/// The HDF5 data must contain the `/ann/vectors`, `/ann/graph_layer_*`,
/// and `/ann/config` datasets as produced by [`to_hdf5_bytes`].
pub fn load_from_hdf5(data: &[u8]) -> Result<Self, FormatError> {
let sig_offset = find_signature(data)?;
let sb = Superblock::parse(data, sig_offset)?;
// Addresses are relative to the superblock: skip any user block.
let (_, data) = split_user_block(data)?;
let sb = Superblock::parse(data, 0)?;
// Read config dataset and its attributes
let config_attrs = read_dataset_attrs(data, &sb, "ann/config")?;
+1
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-bench"
version = "2.7.0"
edition = "2024"
rust-version.workspace = true
description = "Benchmark harnesses for clawhdf5-agent (Track 8)"
license = "MIT"
@@ -396,7 +396,7 @@ fn run_memory_reduction_benchmark() {
println!();
println!(
"{:>8} {:>10} {:>10} {:>10} {:>12}",
"Initial", "Remaining", "Eviction%", "Signal OK?", "BM25 Speedup"
"Initial", "Remaining", "Eviction%", "Signal OK?", "Records ÷"
);
println!("{}", "-".repeat(58));
@@ -440,7 +440,8 @@ 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());
// Rough speedup: BM25 scales roughly linearly with record count
// How many times fewer records there are. Not a measured speedup —
// Part 1 measures search latency before and after.
let speedup = before_count as f64 / after_count.max(1) as f64;
println!(
@@ -480,7 +481,7 @@ fn main() {
println!(" 3. Reducing search latency proportional to record reduction");
println!();
println!(
"Cycle time scales sub-linearly: 100 records ~microseconds, 100K records ~tens of ms."
"Cycle time grows a little faster than linearly: 100 records ~microseconds, 100K records ~tens of ms."
);
println!("Signal records with Correction source + high access_count survive eviction.");
}
@@ -11,12 +11,14 @@
//!
//! Configuration matrix:
//! - Text lengths: short (50 chars), medium (200 chars), long (1000 chars)
//! - Embedding: 384-dim f32 (1536 bytes raw per record)
//! - Embedding: 384-dim, stored as float16 (the default for new stores) or
//! f32 with `--f32`; "raw" bytes are counted as f32 input either way
//! - WAL: enabled and disabled
//!
//! # Usage
//! ```
//! cargo run --release --bin footprint_bench
//! cargo run --release --bin footprint_bench # float16 stores
//! cargo run --release --bin footprint_bench -- --f32 # f32 stores
//! ```
use std::time::Instant;
@@ -24,6 +26,9 @@ 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
@@ -152,6 +157,9 @@ 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");
@@ -241,11 +249,19 @@ 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 f32 = 1,536 bytes raw per record");
println!("Embedding: 384-dim, stored as {stored}; raw input counted as f32");
println!("Text lengths: short=50 chars, medium=200 chars, long=1000 chars");
println!();
@@ -64,6 +64,11 @@ 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 {
@@ -431,6 +436,7 @@ 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);
@@ -940,6 +946,10 @@ 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.
@@ -971,6 +981,9 @@ fn main() {
--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."
@@ -19,6 +19,9 @@
//! cargo run --release -p clawhdf5-bench --bin search_harness -- --full # + 100K
//! cargo run --release -p clawhdf5-bench --bin search_harness -- --json out.json
//! cargo run --release -p clawhdf5-bench --bin search_harness -- --ann-only --uniform
//! cargo run --release -p clawhdf5-bench --bin search_harness -- --float16-study --full
//! cargo run --release -p clawhdf5-bench --bin search_harness -- --options-study --full
//! cargo run --release -p clawhdf5-bench --bin search_harness -- --signing-study --full
//! ```
use std::time::{Duration, Instant};
@@ -88,6 +91,9 @@ static UNIFORM: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::n
/// the memory) instead of f32, to price the recall it costs.
static INT8: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
/// `--f16-first`: in `--float16-study`, run the float16 store first.
static F16_FIRST: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
/// `--rerank`: re-score the candidate pool against the exact vectors before
/// taking the top K.
static RERANK: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
@@ -483,6 +489,394 @@ fn bench_end_to_end(n: usize, json: &mut Vec<serde_json::Value>) {
}));
}
// ---------------------------------------------------------------------------
// Signing study: what does an Ed25519-signed checkpoint cost?
// ---------------------------------------------------------------------------
/// `--signing-study`: checkpoint time unsigned vs signed, `verify` time, and
/// the file-size cost of the stored per-record hashes. Default store
/// settings (float16, int8 index). Medians of five checkpoints / three
/// verifies.
fn signing_study(n: usize) {
use clawhdf5_agent::signing::SigningKey;
let data = make_dataset(n, 0x516 ^ n as u64);
let mut rng = Rng(9);
let entries: Vec<MemoryEntry> = data
.vectors
.iter()
.enumerate()
.map(|(i, v)| MemoryEntry {
chunk: text_for(data.cluster_of[i], i, &mut rng),
embedding: v.clone(),
source_channel: "bench".into(),
timestamp: i as f64,
session_id: format!("s{}", i % 50),
tags: format!("t{i}"),
})
.collect();
let dir = tempfile::tempdir().unwrap();
let path = dir.path().join("sign.h5");
let mut mem = HDF5Memory::create(MemoryConfig::new(path.clone(), "bench", DIM)).unwrap();
mem.save_batch(entries).unwrap();
std::hint::black_box(mem.hybrid_search(&data.queries[0], "", 1.0, 0.0, K));
let median = |mut v: Vec<Duration>| {
v.sort();
v[v.len() / 2]
};
let checkpoint = |mem: &mut HDF5Memory| {
median(
(0..5)
.map(|_| {
let t = Instant::now();
mem.flush_wal().unwrap();
t.elapsed()
})
.collect(),
)
};
let unsigned = checkpoint(&mut mem);
let unsigned_bytes = std::fs::metadata(&path).unwrap().len();
let key = SigningKey::from_bytes(&[7; 32]);
mem.set_signing_key(key.clone());
let signed = checkpoint(&mut mem);
let signed_bytes = std::fs::metadata(&path).unwrap().len();
drop(mem);
let vk = key.verifying_key();
let verify = median(
(0..3)
.map(|_| {
let t = Instant::now();
let r = HDF5Memory::verify(&path, &vk).unwrap();
let d = t.elapsed();
assert!(r.is_valid());
d
})
.collect(),
);
println!(
"| {n} | {:.1} | {:.1} | {:+.1} | {:.1} | {:+.2} |",
millis(unsigned),
millis(signed),
millis(signed) - millis(unsigned),
millis(verify),
(signed_bytes as f64 - unsigned_bytes as f64) / (1024.0 * 1024.0),
);
}
// ---------------------------------------------------------------------------
// Search options study: source filters, re-ranking, confidence rejection
// ---------------------------------------------------------------------------
/// `--options-study`: what `HDF5Memory::search`'s options cost and whether a
/// filtered search finds the right records. Filters keep 50%, 10% or 1% of
/// the store at random, or two whole clusters away from the query (the case
/// the index cannot serve, which falls back to an exact scan). Recall is
/// vector-only against an exact scan of the allowed records; latency is full
/// hybrid search. Hebbian boosting is off.
fn options_study(n: usize) {
use clawhdf5_agent::SearchOptions;
use clawhdf5_agent::confidence::ConfidenceConfig;
use clawhdf5_agent::hybrid::Fusion;
use clawhdf5_agent::reranker::ReRankConfig;
let data = make_dataset(n, 0x0B7 ^ n as u64);
let n_clusters = data.cluster_of.iter().max().map_or(1, |m| m + 1);
let mut rng = Rng(5);
let bucket_of: Vec<usize> = (0..n).map(|_| rng.below(100)).collect();
let bucket = &bucket_of;
let query_texts: Vec<String> = data
.query_cluster
.iter()
.enumerate()
.map(|(i, c)| text_for(*c, i, &mut rng))
.collect();
let exact_top = |q: &[f32], allowed: &dyn 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()
};
// Two stores: channel = random bucket, and channel = cluster.
let dir = tempfile::tempdir().unwrap();
let mut stores = Vec::new();
for by_cluster in [false, true] {
let mut rng = Rng(3);
let entries: Vec<MemoryEntry> = data
.vectors
.iter()
.enumerate()
.map(|(i, v)| MemoryEntry {
chunk: text_for(data.cluster_of[i], i, &mut rng),
embedding: v.clone(),
source_channel: if by_cluster {
format!("c{}", data.cluster_of[i])
} else {
format!("b{}", bucket[i])
},
timestamp: i as f64,
session_id: format!("s{}", i % 50),
tags: format!("t{i}"),
})
.collect();
let mut config = MemoryConfig::new(
dir.path().join(format!("opt_{by_cluster}.h5")),
"bench",
DIM,
);
config.hebbian_boost = 0.0;
let mut mem = HDF5Memory::create(config).unwrap();
mem.save_batch(entries).unwrap();
std::hint::black_box(mem.search(&data.queries[0], "", &SearchOptions::new(K)));
stores.push(mem);
}
let vector_only = SearchOptions::new(K).with_fusion(Fusion::Weighted {
vector: 1.0,
keyword: 0.0,
});
// (label, store, channels for query i, allowed(i, record))
type Case<'a> = (
String,
usize,
Box<dyn Fn(usize) -> Option<Vec<String>> + 'a>,
Box<dyn Fn(usize, usize) -> bool + 'a>,
);
let mut cases: Vec<Case> = vec![(
"no filter".into(),
0,
Box::new(|_| None),
Box::new(|_, _| true),
)];
for pct in [50usize, 10, 1] {
cases.push((
format!("random {pct}%"),
0,
Box::new(move |_| Some((0..pct).map(|b| format!("b{b}")).collect())),
Box::new(move |_, i| bucket[i] < pct),
));
}
let d = &data;
let away = move |qi: usize| {
let qc = d.query_cluster[qi];
[
(qc + n_clusters / 3) % n_clusters,
(qc + 2 * n_clusters / 3) % n_clusters,
]
};
cases.push((
"2 clusters away from the query".into(),
1,
Box::new(move |qi| Some(away(qi).iter().map(|c| format!("c{c}")).collect())),
Box::new(move |qi, i| away(qi).contains(&d.cluster_of[i])),
));
for (label, store, channels, allowed) in &cases {
let mem = &mut stores[*store];
let mut hits = 0;
let mut kept = 0;
for (qi, q) in data.queries.iter().enumerate() {
let mut opts = vector_only.clone();
opts.source_channels = channels(qi);
let got = mem.search(q, "", &opts);
let want = exact_top(q, &|i| allowed(qi, i));
kept += want.len();
hits += got.iter().filter(|r| want.contains(&r.index)).count();
}
let latency = summarize(
(0..N_QUERIES)
.map(|qi| {
let mut opts = SearchOptions::new(K);
opts.source_channels = channels(qi);
let t = Instant::now();
std::hint::black_box(mem.search(&data.queries[qi], &query_texts[qi], &opts));
t.elapsed()
})
.collect(),
);
println!(
"| {n} | {label} | {:.4} | {:.3} | {:.3} |",
hits as f64 / kept.max(1) as f64,
millis(latency.p50),
millis(latency.p99),
);
}
let mem = &mut stores[0];
for (label, opts) in [
(
"re-rank",
SearchOptions::new(K).with_rerank(ReRankConfig::default()),
),
(
"re-rank + confidence",
SearchOptions::new(K)
.with_rerank(ReRankConfig::default())
.with_confidence(ConfidenceConfig::default()),
),
] {
let latency = summarize(
(0..N_QUERIES)
.map(|qi| {
let t = Instant::now();
std::hint::black_box(mem.search(&data.queries[qi], &query_texts[qi], &opts));
t.elapsed()
})
.collect(),
);
println!(
"| {n} | {label} | — | {:.3} | {:.3} |",
millis(latency.p50),
millis(latency.p99)
);
}
}
// ---------------------------------------------------------------------------
// float16 study: what does half-precision embedding storage cost?
// ---------------------------------------------------------------------------
/// `--float16-study`: the same data in an `f32` store and a `float16` store.
/// Reports file size, checkpoint and open time, vector-search recall@10
/// against an exact scan of the *original* f32 vectors, how often the two
/// stores return the same top 10, and `hybrid_search` latency. Hebbian
/// boosting is off, so every query sees the same store.
fn float16_study(n: usize) {
let data = make_dataset(n, 0xF16 ^ n as u64);
let mut rng = Rng(11);
let query_texts: Vec<String> = data
.query_cluster
.iter()
.enumerate()
.map(|(i, c)| text_for(*c, i, &mut rng))
.collect();
// Exact top K by cosine (the vectors are unit length) on the f32 inputs.
let exact: Vec<Vec<usize>> = data
.queries
.iter()
.map(|q| {
let mut scored: Vec<(usize, f32)> = data
.vectors
.iter()
.enumerate()
.map(|(i, v)| (i, v.iter().zip(q).map(|(a, b)| a * b).sum()))
.collect();
scored.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
scored.into_iter().take(K).map(|(i, _)| i).collect()
})
.collect();
let dir = tempfile::tempdir().unwrap();
let mut per_variant: Vec<(bool, Vec<Vec<usize>>)> = Vec::new();
// `--f16-first` swaps the order, to check the numbers do not depend on
// which store runs first (page cache, allocator, CPU frequency).
let order = if F16_FIRST.load(std::sync::atomic::Ordering::Relaxed) {
[true, false]
} else {
[false, true]
};
for float16 in order {
let path = dir.path().join(format!("f16study_{float16}.h5"));
let mut rng = Rng(3);
let entries: Vec<MemoryEntry> = data
.vectors
.iter()
.enumerate()
.map(|(i, v)| MemoryEntry {
chunk: text_for(data.cluster_of[i], i, &mut rng),
embedding: v.clone(),
source_channel: "bench".into(),
timestamp: i as f64,
session_id: format!("s{}", i % 50),
tags: format!("t{i}"),
})
.collect();
let mut config = MemoryConfig::new(path.clone(), "bench", DIM);
config.float16 = float16;
config.hebbian_boost = 0.0;
let mut mem = HDF5Memory::create(config).unwrap();
mem.save_batch(entries).unwrap();
// Build the indexes, then time a checkpoint that writes everything.
std::hint::black_box(mem.hybrid_search(&data.queries[0], "", 1.0, 0.0, K));
let t = Instant::now();
mem.flush_wal().unwrap();
let checkpoint = t.elapsed();
drop(mem);
let file_bytes = std::fs::metadata(&path).unwrap().len();
// Median of three opens.
let mut opens: Vec<Duration> = (0..3)
.map(|_| {
let t = Instant::now();
let m = HDF5Memory::open(&path).unwrap();
let d = t.elapsed();
drop(m);
d
})
.collect();
opens.sort();
let mut mem = HDF5Memory::open(&path).unwrap();
// Vector-only search: empty text, all weight on the vector stage.
let results: Vec<Vec<usize>> = data
.queries
.iter()
.map(|q| {
mem.hybrid_search(q, "", 1.0, 0.0, K)
.iter()
.map(|r| r.index)
.collect()
})
.collect();
let hits: usize = results
.iter()
.zip(&exact)
.map(|(got, want)| got.iter().filter(|i| want.contains(i)).count())
.sum();
let recall = hits as f64 / (K * data.queries.len()) as f64;
let latency = summarize(
(0..N_QUERIES)
.map(|i| {
let t = Instant::now();
std::hint::black_box(mem.hybrid_search(
&data.queries[i],
&query_texts[i],
0.4,
0.6,
K,
));
t.elapsed()
})
.collect(),
);
let overlap = match per_variant.first() {
Some((_, other)) => {
let same: usize = results
.iter()
.zip(other)
.map(|(a, b)| a.iter().filter(|i| b.contains(i)).count())
.sum();
format!("{:.4}", same as f64 / (K * data.queries.len()) as f64)
}
None => "—".into(),
};
println!(
"| {n} | {} | {:.1} | {:.0} | {:.1} | {recall:.4} | {overlap} | {:.3} |",
if float16 { "float16" } else { "f32" },
mib(file_bytes),
millis(checkpoint),
millis(opens[1]),
millis(latency.p50),
);
per_variant.push((float16, results));
}
}
// ---------------------------------------------------------------------------
// Fusion study: does capping the keyword candidate pool change the ranking?
// ---------------------------------------------------------------------------
@@ -642,6 +1036,52 @@ fn main() {
}
return;
}
if args.iter().any(|a| a == "--signing-study") {
println!("## Signed checkpoints ({DIM}-dim, float16, int8 index)\n");
println!(
"| N | checkpoint ms, unsigned | checkpoint ms, signed | signing adds ms | verify ms | file MiB added |"
);
println!("|---:|---:|---:|---:|---:|---:|");
for &n in if full {
&[1_000, 10_000, 100_000][..]
} else {
&[1_000, 10_000][..]
} {
signing_study(n);
}
return;
}
if args.iter().any(|a| a == "--options-study") {
println!("## Search options ({DIM}-dim, k = {K}, Hebbian boost off)\n");
println!("| N | options | filtered recall@10 | p50 ms | p99 ms |");
println!("|---:|---|---:|---:|---:|");
for &n in if full {
&[10_000, 100_000][..]
} else {
&[10_000][..]
} {
options_study(n);
}
return;
}
if args.iter().any(|a| a == "--f16-first") {
F16_FIRST.store(true, std::sync::atomic::Ordering::Relaxed);
}
if args.iter().any(|a| a == "--float16-study") {
println!("## float16 embedding storage ({DIM}-dim, int8 index, Hebbian boost off)\n");
println!(
"| N | embeddings | file MiB | checkpoint ms | open ms | recall@10 | top-10 overlap with the other | hybrid p50 ms |"
);
println!("|---:|---|---:|---:|---:|---:|---:|---:|");
for &n in if full {
&[1_000, 10_000, 100_000][..]
} else {
&[1_000, 10_000][..]
} {
float16_study(n);
}
return;
}
if args.iter().any(|a| a == "--int8") {
INT8.store(true, std::sync::atomic::Ordering::Relaxed);
println!("(int8-quantised index vectors)");
+1
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-cli"
version = "2.7.0"
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"
+158 -21
View File
@@ -1,15 +1,22 @@
use std::path::PathBuf;
use std::path::{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
/// Path to the .h5 memory file (not needed for `keygen`)
#[arg(short, long, env = "CLAWHDF5_PATH")]
path: PathBuf,
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>,
#[command(subcommand)]
command: Commands,
@@ -28,10 +35,22 @@ enum Commands {
/// Enable write-ahead log
#[arg(long)]
wal: bool,
/// Store the vector index's copy of the embeddings as int8, roughly
/// halving a loaded store's memory at about 13% fewer queries/second
/// 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 {
@@ -79,6 +98,38 @@ 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() {
@@ -91,24 +142,76 @@ 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,
quantized_index,
f32_index,
quantized_index: _,
f32,
float16: _,
} => {
let mut config = MemoryConfig::new(cli.path.clone(), &agent_id, dim);
let mut config = MemoryConfig::new(path.clone(), &agent_id, dim);
config.wal_enabled = wal;
config.quantized_index = quantized_index;
let mem = HDF5Memory::create(config)?;
// 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 j = serde_json::json!({
"status": "created",
"path": cli.path.display().to_string(),
"path": path.display().to_string(),
"agent_id": agent_id,
"embedding_dim": dim,
"wal_enabled": wal,
"quantized_index": quantized_index,
"quantized_index": config_quantized,
"float16": config_float16,
"signed": mem.is_signed(),
"count": mem.count(),
});
println!("{}", serde_json::to_string_pretty(&j)?);
@@ -125,7 +228,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
};
let entry: MemoryEntry = serde_json::from_str(&input)?;
let mut mem = HDF5Memory::open(&cli.path)?;
let mut mem = open_writable(&path, &key)?;
let idx = mem.save(entry)?;
let j = serde_json::json!({ "status": "saved", "index": idx, "count": mem.count() });
println!("{}", serde_json::to_string(&j)?);
@@ -139,7 +242,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
keyword_weight,
} => {
let emb: Vec<f32> = serde_json::from_str(&embedding)?;
let mut mem = HDF5Memory::open(&cli.path)?;
let mut mem = open_writable(&path, &key)?;
let results = mem.hybrid_search(&emb, &query, vector_weight, keyword_weight, top_k);
let j: Vec<serde_json::Value> = results
.iter()
@@ -157,7 +260,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::Recall { index } => {
let mem = HDF5Memory::open_read_only(&cli.path)?;
let mem = HDF5Memory::open_read_only(&path)?;
match mem.get_chunk(index) {
Some(content) => {
let j = serde_json::json!({ "index": index, "chunk": content });
@@ -171,22 +274,23 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::Stats => {
let mem = HDF5Memory::open_read_only(&cli.path)?;
let mem = HDF5Memory::open_read_only(&path)?;
let cfg = mem.config();
let j = serde_json::json!({
"path": cli.path.display().to_string(),
"path": 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 = HDF5Memory::open(&cli.path)?;
let mut mem = open_writable(&path, &key)?;
let before = mem.wal_pending_count();
mem.flush_wal()?;
let j = serde_json::json!({
@@ -198,7 +302,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::AgentsMd { output } => {
let mem = HDF5Memory::open_read_only(&cli.path)?;
let mem = HDF5Memory::open_read_only(&path)?;
let md = mem.generate_agents_md();
match output {
Some(p) => {
@@ -210,7 +314,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
}
Commands::Export => {
let mem = HDF5Memory::open_read_only(&cli.path)?;
let mem = HDF5Memory::open_read_only(&path)?;
for i in 0..mem.count() {
if let Some(chunk) = mem.get_chunk(i) {
let j = serde_json::json!({ "index": i, "chunk": chunk });
@@ -219,11 +323,44 @@ 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(&cli.path, &dest)?;
let _result = clawhdf5_agent::storage::snapshot_file(&path, &dest)?;
let j = serde_json::json!({
"status": "snapshot_created",
"source": cli.path.display().to_string(),
"source": path.display().to_string(),
"dest": dest.display().to_string(),
});
println!("{}", serde_json::to_string(&j)?);
+1
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-derive"
version = "2.7.0"
edition = "2024"
rust-version.workspace = true
description = "Derive macros for rustyhdf5 HDF5 traits"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
+7 -2
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-filters"
version = "2.7.0"
edition = "2024"
rust-version.workspace = true
description = "Filter and compression pipeline for clawhdf5"
license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
@@ -25,8 +26,12 @@ name = "compression_bench"
harness = false
[features]
default = ["fast-deflate"]
# Pure-Rust zlib-rs by default; `fast-deflate` (zlib-ng, C) overrides it.
default = ["zlib-rs"]
fast-deflate = ["flate2/zlib-ng"]
system-zlib = ["flate2/zlib-default"]
zlib-rs = ["flate2/zlib-rs"]
# `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"]
apple-compression = []
+6 -4
View File
@@ -8,16 +8,18 @@ Filter and compression pipeline for clawhdf5.
## Features
- DEFLATE compression/decompression
- Fast deflate via zlib-ng (`fast-deflate` feature)
- Pure-Rust deflate via zlib-rs (default, `zlib-rs` feature)
- zlib-ng instead, if you want it (`fast-deflate` feature; C, needs cmake)
- Apple Compression framework support (`apple-compression` feature)
## Usage
```rust
use clawhdf5_filters::{deflate_decode, deflate_encode};
use clawhdf5_filters::{deflate_compress, deflate_decompress};
let compressed = deflate_encode(&data, 6).unwrap();
let decompressed = deflate_decode(&compressed).unwrap();
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();
```
## License
+111 -48
View File
@@ -1,12 +1,13 @@
//! Fast deflate backends: Apple Compression Framework and zlib-ng.
//! Deflate backends: Apple Compression Framework, zlib-ng and zlib-rs.
//!
//! Backend selection priority (decompression & compression):
//! 1. Apple Compression Framework (macOS only, `apple-compression` feature)
//! 2. flate2 with zlib-ng backend (`fast-deflate` feature) or miniz_oxide (default)
//! 2. flate2 with zlib-ng (`fast-deflate`), else zlib-rs (`zlib-rs`, the
//! default), else miniz_oxide
//!
//! The Apple Compression Framework uses hardware-accelerated zlib on Apple Silicon
//! and is typically the fastest option on macOS. zlib-ng is the fastest portable
//! option and what C HDF5 uses internally.
//! 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.
// ---------------------------------------------------------------------------
// Apple Compression Framework FFI (macOS only)
@@ -243,65 +244,117 @@ mod apple {
}
// ---------------------------------------------------------------------------
// Streaming decompression via flate2 (uses zlib-ng when fast-deflate enabled)
// 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 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.
/// 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.
pub(crate) fn flate2_decompress_preallocated(
data: &[u8],
output_size: usize,
) -> Result<Vec<u8>, String> {
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)
inflate_bounded(data, output_size, output_size)
}
/// 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;
/// 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").
/// Decompress with no size hint, bounded by [`MAX_DECOMPRESS_SIZE`] so a
/// hostile zlib stream cannot force arbitrarily large allocation (a "zlib
/// bomb").
pub(crate) fn flate2_decompress_streaming(data: &[u8]) -> Result<Vec<u8>, String> {
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!(
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!(
"decompressed output exceeds {} MiB limit",
MAX_DECOMPRESS_SIZE / 1024 / 1024
));
)
} else {
e
}
Ok(result)
})
}
/// Compress data using flate2 (zlib-ng when fast-deflate enabled, else miniz_oxide).
/// 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).
pub(crate) fn flate2_compress(data: &[u8], level: u32) -> Result<Vec<u8>, String> {
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())
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());
}
}
}
}
}
// ---------------------------------------------------------------------------
@@ -312,7 +365,7 @@ 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`, otherwise miniz_oxide)
/// 2. flate2 (zlib-ng with `fast-deflate`, else zlib-rs, else miniz_oxide)
///
/// When `output_hint` > 0, pre-allocates the output buffer for zero-copy
/// decompression (avoids reallocation).
@@ -344,7 +397,7 @@ 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`, otherwise miniz_oxide)
/// 2. flate2 (zlib-ng with `fast-deflate`, else zlib-rs, else miniz_oxide)
pub fn compress(data: &[u8], level: u32) -> Result<Vec<u8>, String> {
#[cfg(all(target_os = "macos", feature = "apple-compression"))]
{
@@ -377,9 +430,19 @@ 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 = "fast-deflate",
feature = "zlib-rs"
)))]
{
"miniz_oxide"
@@ -436,7 +499,7 @@ mod tests {
fn backend_name_is_set() {
let name = active_backend();
assert!(
["miniz_oxide", "zlib-ng", "apple-compression"].contains(&name),
["miniz_oxide", "zlib-rs", "zlib-ng", "apple-compression"].contains(&name),
"unexpected backend: {name}"
);
}
+6 -4
View File
@@ -2,12 +2,14 @@
//!
//! Provides deflate (zlib) decompression/compression with multiple backend options:
//!
//! - **Default**: `miniz_oxide` (pure Rust, no C dependencies)
//! - **`fast-deflate` feature**: `zlib-ng` via flate2 (~2-3x faster, matches C HDF5)
//! - **Default (`zlib-rs` feature)**: `zlib-rs` via flate2 (pure Rust, no C
//! dependencies)
//! - **`fast-deflate` feature**: `zlib-ng` via flate2 (C, built with cmake)
//! - **`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 > miniz_oxide.
//! Backend priority: apple-compression > zlib-ng > zlib-rs > miniz_oxide.
pub mod fast_deflate;
@@ -115,7 +117,7 @@ mod tests {
fn backend_reports_name() {
let name = deflate_backend();
assert!(
["miniz_oxide", "zlib-ng", "apple-compression"].contains(&name),
["miniz_oxide", "zlib-rs", "zlib-ng", "apple-compression"].contains(&name),
"unexpected backend: {name}"
);
}
+10 -2
View File
@@ -2,6 +2,7 @@
name = "clawhdf5-format"
version = "2.7.0"
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"
@@ -23,6 +24,7 @@ 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" }
@@ -32,7 +34,10 @@ name = "bench"
harness = false
[features]
default = ["std", "checksum", "deflate", "provenance", "fast-deflate", "system-zlib-decompress"]
# 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"]
std = []
checksum = []
deflate = ["flate2"]
@@ -42,7 +47,10 @@ fast-checksum = ["crc32fast"]
fast-deflate = ["flate2/zlib-ng"]
system-zlib = ["flate2/zlib-default"]
system-zlib-decompress = []
zlib-rs = ["flate2/zlib-rs"]
# `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"]
lz4 = ["lz4_flex"]
zstd = ["dep:zstd"]
blake3_hash = ["blake3"]
+83 -33
View File
@@ -97,7 +97,7 @@ impl AttributeMessage {
return Ok(Cow::Borrowed(bytes));
}
let (file_data, offset_size) = file.ok_or(FormatError::UnresolvedSharedMessage)?;
let shared_ref = shared_message::parse_shared_ref(bytes, offset_size)?;
let shared_ref = shared_message::parse_shared_ref_sized(bytes, offset_size, length_size)?;
shared_message::resolve_shared_message(
file_data,
&shared_ref,
@@ -394,42 +394,80 @@ 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 {
if shared_message::is_shared(msg.flags) {
let attr = if shared_message::is_shared(msg.flags) {
// Shared attribute: resolve the reference to get actual attribute data
let shared_ref = shared_message::parse_shared_ref(&msg.data, offset_size)?;
let resolved_data = shared_message::resolve_shared_message(
shared_message::parse_shared_ref_sized(&msg.data, offset_size, length_size)
.and_then(|shared_ref| {
shared_message::resolve_shared_message(
file_data,
&shared_ref,
MessageType::Attribute,
offset_size,
length_size,
)?;
let attr = AttributeMessage::parse_in_file(
&resolved_data,
)
})
.and_then(|resolved| {
AttributeMessage::parse_in_file(
&resolved,
file_data,
offset_size,
length_size,
)?;
attrs.push(attr);
)
})
} else {
let attr = AttributeMessage::parse_in_file(
&msg.data,
file_data,
offset_size,
length_size,
)?;
attrs.push(attr);
AttributeMessage::parse_in_file(&msg.data, file_data, offset_size, length_size)
};
match attr {
Ok(attr) => attrs.push(attr),
Err(e) => on_error(e)?,
}
}
}
@@ -439,9 +477,15 @@ pub fn extract_attributes_full(
if let Some(info) = attr_info
&& let Some(fh_addr) = info.fractal_heap_address
{
let dense_attrs =
extract_dense_attributes(file_data, &info, fh_addr, offset_size, length_size)?;
attrs.extend(dense_attrs);
extract_dense_attributes(
file_data,
&info,
fh_addr,
offset_size,
length_size,
&mut attrs,
on_error,
)?;
}
Ok(attrs)
@@ -468,7 +512,9 @@ fn extract_dense_attributes(
fh_addr: u64,
offset_size: u8,
length_size: u8,
) -> Result<Vec<AttributeMessage>, FormatError> {
attrs: &mut Vec<AttributeMessage>,
on_error: &mut dyn FnMut(FormatError) -> Result<(), FormatError>,
) -> Result<(), FormatError> {
// Parse fractal heap
let fh = FractalHeapHeader::parse(file_data, fh_addr as usize, offset_size, length_size)?;
@@ -482,28 +528,32 @@ 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_offset = 0;
if record.data.len() < id_offset + fh.heap_id_length as usize {
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(),
})?;
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 =
AttributeMessage::parse_in_file(&attr_data, file_data, offset_size, length_size)?;
attrs.push(attr);
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)?,
}
}
Ok(attrs)
Ok(())
}
#[cfg(test)]
+64 -40
View File
@@ -323,39 +323,21 @@ fn collect_internal_records(
let records_start = pos;
pos += records_total;
// Compute sizes for child pointers
// max_records at child depth - for variable-width nrec encoding
// 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.
let child_depth = depth - 1;
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 nrec_width = bytes_for_max_records(max_leaf_nrec);
let total_nrec_width = if depth > 1 {
// 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)
bytes_for_max_records(cum_max_records(
node_size,
record_size,
offset_size,
max_leaf_nrec,
child_depth,
))
} else {
0
};
@@ -435,14 +417,36 @@ fn collect_internal_records(
Ok(())
}
/// 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));
/// 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;
}
total
cum
}
#[cfg(test)]
@@ -512,9 +516,15 @@ mod tests {
child_nrec: u64,
) -> Vec<u8> {
let max_leaf = max_records_leaf(node_size, record_size);
let nrec_width = bytes_for_max_records(if depth == 1 { max_leaf } else { max_leaf * 2 });
let nrec_width = bytes_for_max_records(max_leaf);
let total_width = if depth > 1 {
bytes_for_max_records(header_max_total_records(max_leaf, depth - 1))
bytes_for_max_records(cum_max_records(
node_size,
record_size,
8,
max_leaf,
depth - 1,
))
} else {
0
};
@@ -673,4 +683,18 @@ 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);
}
}
+516 -271
View File
@@ -223,13 +223,32 @@ 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 {
coord: ChunkCoord,
key: SlotKey,
/// Shared so a cache hit is a refcount bump, not a copy of the whole
/// (potentially large) decompressed chunk.
data: Arc<CacheAlignedBuffer>,
@@ -237,21 +256,48 @@ 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-dataset chunk cache with hash-based index and LRU eviction.
/// A per-file chunk cache: chunk indexes per dataset, plus an LRU of
/// decompressed chunks, all keyed by dataset.
///
/// # Usage
/// 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).
///
/// ```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).
/// Memory is bounded: decompressed data by `max_bytes`/`max_slots` across
/// all datasets, indexes by [`MAX_INDEXED_DATASETS`] and
/// [`MAX_INDEXED_CHUNKS`].
///
/// Only available with the `std` feature because it requires `std::sync::Mutex`.
#[cfg(feature = "std")]
@@ -261,26 +307,20 @@ pub struct ChunkCache {
#[cfg(feature = "std")]
struct CacheInner {
/// Hash index: chunk coordinate -> ChunkInfo (offset + size in file).
/// Populated once per dataset on first access.
index: Option<HashMap<ChunkCoord, ChunkInfo>>,
/// Per-dataset chunk indexes, keyed by chunk-index address.
datasets: HashMap<u64, DatasetEntry>,
/// 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>,
/// Dataset the address-less methods act on (see `ensure_dataset`).
current: Option<u64>,
/// LRU cache of decompressed chunk data.
slots: Vec<CachedChunk>,
/// Coordinate -> index into `slots`, for O(1) lookup instead of a linear
/// Key -> 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<ChunkCoord, usize>,
slot_index: HashMap<SlotKey, usize>,
/// Current total bytes of cached decompressed data.
current_bytes: usize,
@@ -294,17 +334,145 @@ struct CacheInner {
/// Monotonic counter for LRU ordering.
tick: u64,
/// Last accessed chunk coordinate (for sequential detection).
last_coord: Option<ChunkCoord>,
/// Last accessed chunk (for sequential detection).
last_coord: Option<SlotKey>,
/// Access pattern statistics.
stats: AccessStats,
}
/// Pre-built chunk index for O(1) coordinate lookups.
chunk_index: Option<ChunkIndex>,
#[cfg(feature = "std")]
impl CacheInner {
fn current(&self) -> u64 {
self.current.unwrap_or(UNBOUND_DATASET)
}
/// Pre-computed chunk layout for fast assembly.
chunk_layout: Option<ChunkLayout>,
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
}
}
/// Access pattern statistics tracked by the chunk cache.
@@ -356,8 +524,8 @@ impl ChunkCache {
pub fn with_capacity(max_bytes: usize, max_slots: usize) -> Self {
Self {
inner: std::sync::Mutex::new(CacheInner {
index: None,
index_addr: None,
datasets: HashMap::new(),
current: None,
slots: Vec::with_capacity(max_slots.min(64)),
slot_index: HashMap::with_capacity(max_slots.min(64)),
current_bytes: 0,
@@ -366,340 +534,331 @@ impl ChunkCache {
tick: 0,
last_coord: None,
stats: AccessStats::default(),
chunk_index: None,
chunk_layout: None,
}),
}
}
// ----- Index operations -----
fn lock(&self) -> std::sync::MutexGuard<'_, CacheInner> {
self.inner.lock().unwrap_or_else(|e| e.into_inner())
}
/// The most decompressed bytes this cache will hold.
pub fn max_bytes(&self) -> usize {
self.inner.lock().map(|g| g.max_bytes).unwrap_or(0)
self.lock().max_bytes
}
/// Bind the cache to the dataset at chunk-index address `addr`.
// ----- Dataset-keyed operations (safe to use concurrently) -----
/// The chunk list of the dataset whose chunk index is at `addr`.
///
/// 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.
/// 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.
pub fn ensure_dataset(&self, addr: u64) -> bool {
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
let mut inner = self.lock();
let changed = inner.current != Some(addr);
inner.current = Some(addr);
changed
}
/// Returns `true` if the chunk index has been built.
/// Returns `true` if the bound dataset's chunk index has been built.
pub fn has_index(&self) -> bool {
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.index
.is_some()
let inner = self.lock();
inner
.entry(inner.current())
.is_some_and(|e| e.index.is_some())
}
/// Build the chunk index from a pre-collected list of `ChunkInfo`.
/// Build the bound dataset's 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 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);
let addr = self.lock().current();
let _ = self.index_for::<core::convert::Infallible>(addr, rank, || Ok(chunks.to_vec()));
}
/// Look up a chunk by its spatial coordinate in the index.
/// Look up a chunk by its spatial coordinate in the bound dataset's index.
pub fn lookup_index(&self, coord: &[u64]) -> Option<ChunkInfo> {
let inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.index.as_ref()?.get(coord).cloned()
let inner = self.lock();
inner
.entry(inner.current())?
.index
.as_ref()?
.get(coord)
.cloned()
}
/// Return all indexed chunks as a `Vec<ChunkInfo>` (order unspecified).
/// Return all of the bound dataset's indexed chunks (order unspecified).
pub fn all_indexed_chunks(&self) -> Option<Vec<ChunkInfo>> {
let inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.index.as_ref().map(|m| m.values().cloned().collect())
let inner = self.lock();
let index = inner.entry(inner.current())?.index.as_ref()?;
Some(index.values().cloned().collect())
}
// ----- Chunk index (pre-built coordinate → ChunkInfo map) -----
/// Returns `true` if the chunk B-tree index has been built.
/// Returns `true` if the bound dataset's `ChunkIndex` has been built.
pub fn has_chunk_index(&self) -> bool {
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.chunk_index
.is_some()
let inner = self.lock();
inner
.entry(inner.current())
.is_some_and(|e| e.chunk_index.is_some())
}
/// Build and store the chunk B-tree index from a pre-collected list of `ChunkInfo`.
/// Build and store the bound dataset's `ChunkIndex`.
pub fn populate_chunk_index(&self, chunks: &[ChunkInfo], rank: usize) {
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
if inner.chunk_index.is_some() {
return;
}
inner.chunk_index = Some(ChunkIndex::build(chunks, rank));
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);
}
// ----- Chunk layout (pre-computed assembly plan) -----
/// Returns `true` if the chunk layout has been computed.
/// Returns `true` if the bound dataset's 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()
let inner = self.lock();
inner
.entry(inner.current())
.is_some_and(|e| e.chunk_layout.is_some())
}
/// Build and store the pre-computed chunk layout for fast assembly.
/// 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.inner.lock().unwrap_or_else(|e| e.into_inner());
if inner.chunk_layout.is_some() {
let mut inner = self.lock();
let addr = inner.current();
let entry = inner.touch(addr);
if entry.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));
if let Some(idx) = entry.chunk_index.clone() {
entry.chunk_layout = Some(Arc::new(ChunkLayout::build(
&idx, ds_dims, chunk_dims, elem_size,
)));
}
}
/// Execute a function with a reference to the chunk layout.
///
/// Returns `None` if the layout hasn't been computed yet.
/// Execute a function with a reference to the bound dataset's 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 inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.chunk_layout.as_ref().map(f)
let layout = {
let inner = self.lock();
inner.entry(inner.current())?.chunk_layout.clone()?
};
Some(f(&layout))
}
// ----- Decompressed data cache (LRU) -----
/// Try to get cached decompressed data for a chunk coordinate.
/// Try to get cached decompressed data for a chunk of the bound dataset.
///
/// 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.
/// Returns an owned copy; prefer [`Self::get_decompressed_aligned`] when
/// an `Arc`-shared buffer works for the caller.
pub fn get_decompressed(&self, coord: &[u64]) -> Option<Vec<u8>> {
self.get_decompressed_aligned(coord)
.map(|arc| arc.as_slice().to_vec())
}
/// 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.
/// Reference-counted cached buffer for a chunk of the bound dataset.
pub fn get_decompressed_aligned(&self, coord: &[u64]) -> Option<Arc<CacheAlignedBuffer>> {
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
let mut inner = self.lock();
let addr = inner.current();
inner.get_decompressed(addr, coord)
}
/// 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.
/// Insert decompressed chunk data for the bound dataset into the LRU
/// cache, returning the `Arc`-shared buffer now cached.
pub fn put_decompressed(&self, coord: ChunkCoord, data: Vec<u8>) -> Arc<CacheAlignedBuffer> {
let aligned = CacheAlignedBuffer::from_vec(data);
self.put_decompressed_aligned(coord, aligned)
self.put_decompressed_aligned(coord, CacheAlignedBuffer::from_vec(data))
}
/// 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).
/// Insert an already-aligned buffer for the bound dataset.
pub fn put_decompressed_aligned(
&self,
coord: ChunkCoord,
data: CacheAlignedBuffer,
) -> Arc<CacheAlignedBuffer> {
let data = Arc::new(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;
let mut inner = self.lock();
let addr = inner.current();
inner.put_decompressed((addr, coord), data)
}
// 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
/// [`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);
}
// 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;
}
// ----- Whole-cache operations -----
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).
/// Clear the entire cache (indexes + decompressed data + stats).
pub fn clear(&self) {
let mut inner = self.inner.lock().unwrap_or_else(|e| e.into_inner());
inner.index = None;
inner.index_addr = None;
let mut inner = self.lock();
inner.datasets.clear();
inner.current = 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.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.stats
.clone()
self.lock().stats.clone()
}
/// Update the sweep direction label in the access stats.
pub fn set_sweep_direction(&self, direction: &'static str) {
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.stats
.sweep_direction = Some(direction);
self.lock().stats.sweep_direction = Some(direction);
}
/// Number of decompressed chunks currently cached.
/// Number of decompressed chunks currently cached (all datasets).
pub fn cached_chunk_count(&self) -> usize {
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.slots
.len()
self.lock().slots.len()
}
/// Total bytes of decompressed data currently cached.
/// Total bytes of decompressed data currently cached (all datasets).
pub fn cached_bytes(&self) -> usize {
self.inner
.lock()
.unwrap_or_else(|e| e.into_inner())
.current_bytes
self.lock().current_bytes
}
/// Number of datasets whose chunk index is currently kept.
pub fn indexed_dataset_count(&self) -> usize {
self.lock().datasets.len()
}
}
@@ -808,6 +967,92 @@ 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();
+200
View File
@@ -0,0 +1,200 @@
//! 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());
}
}
+157 -98
View File
@@ -15,7 +15,7 @@ use crate::datatype::Datatype;
use crate::error::FormatError;
use crate::extensible_array::{ExtensibleArrayHeader, read_extensible_array_chunks};
use crate::filter_pipeline::FilterPipeline;
use crate::filters::decompress_chunk;
use crate::filters::{all_filters_skipped, decompress_chunk_masked};
use crate::fixed_array::{FixedArrayHeader, read_fixed_array_chunks};
#[cfg(feature = "std")]
use std::sync::Arc;
@@ -65,11 +65,13 @@ fn decompress_all_chunks(
let raw_chunk = &file_data[c_addr..c_addr + size];
let decompressed = if let Some(pl) = pipeline {
if chunk_info.filter_mask == 0 {
decompress_chunk(raw_chunk, pl, chunk_total_bytes, element_size)?
} else {
raw_chunk.to_vec()
}
decompress_chunk_masked(
raw_chunk,
pl,
chunk_total_bytes,
element_size,
chunk_info.filter_mask,
)?
} else {
raw_chunk.to_vec()
};
@@ -223,6 +225,10 @@ pub fn collect_chunk_info(
collect_chunk_info_inner(file_data, btree_address, ndims, offset_size, length_size, 0)
}
/// Width of each chunk offset in a v1 chunk B-tree key, independent of the
/// file's size-of-offsets.
const CHUNK_KEY_OFFSET_SIZE: u8 = 8;
/// Maximum recursion depth for chunk B-tree traversal (malformed/cyclic data
/// protection), matching `btree_v1.rs`'s `MAX_BTREE_DEPTH`.
const MAX_CHUNK_BTREE_DEPTH: usize = 64;
@@ -260,8 +266,14 @@ fn collect_chunk_info_inner(
let mut pos = offset + 8 + os * 2; // skip left/right sibling
// Key size: chunk_size(4) + filter_mask(4) + ndims * offset_size
let key_size = 4 + 4 + ndims * os;
// Key: chunk_size(4) + filter_mask(4) + one offset per dimension. The
// offsets are always 8 bytes each — they are dataset coordinates, not file
// addresses, so they do not follow the superblock's size-of-offsets (only
// the sibling and child addresses do).
let key_size = ndims
.checked_mul(CHUNK_KEY_OFFSET_SIZE as usize)
.and_then(|n| n.checked_add(8))
.ok_or_else(|| FormatError::ChunkedReadError("chunk key too large".into()))?;
if node_level == 0 {
// Leaf node: keys and children interleaved
@@ -287,8 +299,8 @@ fn collect_chunk_info_inner(
let mut offsets = Vec::with_capacity(ndims);
let mut kp = pos + 8;
for _ in 0..ndims {
offsets.push(read_offset(file_data, kp, offset_size)?);
kp += os;
offsets.push(read_offset(file_data, kp, CHUNK_KEY_OFFSET_SIZE)?);
kp += CHUNK_KEY_OFFSET_SIZE as usize;
}
pos += key_size;
@@ -507,6 +519,7 @@ pub fn list_chunks(
addr_opt,
single_filtered_size,
single_filter_mask,
unfiltered_edges,
) = match layout {
DataLayout::Chunked {
chunk_dimensions,
@@ -515,6 +528,7 @@ pub fn list_chunks(
chunk_index_type,
single_chunk_filtered_size,
single_chunk_filter_mask,
dont_filter_partial_edge_chunks,
} => (
chunk_dimensions,
*version,
@@ -522,6 +536,7 @@ pub fn list_chunks(
*btree_address,
*single_chunk_filtered_size,
*single_chunk_filter_mask,
*dont_filter_partial_edge_chunks,
),
_ => {
return Err(FormatError::ChunkedReadError(
@@ -554,7 +569,7 @@ pub fn list_chunks(
}
// Collect chunks based on version and index type
let chunks = match (version, chunk_index_type) {
let mut chunks = match (version, chunk_index_type) {
(3, _) => {
let ndims = chunk_dimensions.len(); // rank+1
collect_chunk_info(file_data, addr, ndims, offset_size, length_size)?
@@ -593,6 +608,7 @@ pub fn list_chunks(
file_data,
&header,
&dataspace.dimensions,
dataspace.max_dimensions.as_deref(),
spatial_chunk_dims,
elem_size as u32,
offset_size,
@@ -608,6 +624,7 @@ pub fn list_chunks(
file_data,
&header,
&dataspace.dimensions,
dataspace.max_dimensions.as_deref(),
spatial_chunk_dims,
elem_size as u32,
offset_size,
@@ -633,6 +650,23 @@ pub fn list_chunks(
}
};
// With "don't filter partial edge chunks", a chunk that extends past the
// dataset's extent is stored raw while its filter mask still reads 0.
// Mark every filter skipped so all read paths copy it as-is.
if unfiltered_edges {
for chunk in &mut chunks {
let partial = chunk
.offsets
.iter()
.zip(&chunk_dims)
.zip(&ds_dims)
.any(|((&off, &cd), &dd)| off.saturating_add(cd as u64) > dd as u64);
if partial {
chunk.filter_mask = u32::MAX;
}
}
}
Ok((chunks, chunk_dims))
}
@@ -804,24 +838,20 @@ pub fn read_chunked_data_cached(
)));
}
// The per-file cache is shared across datasets; bind it to this one so a
// different dataset's chunk index is never reused for this read.
cache.ensure_dataset(addr);
// Populate chunk index on first access
if !cache.has_index() {
let (chunks, _) = list_chunks(
// The per-file cache is shared across datasets (and threads); every
// lookup is keyed by this dataset's chunk-index address, so another
// dataset's index or chunks are never used for this read.
let chunks = cache.chunks_for(addr, rank, || {
list_chunks(
file_data,
layout,
dataspace,
elem_size,
offset_size,
length_size,
)?;
cache.populate_index(&chunks, rank);
}
let chunks = cache.all_indexed_chunks().unwrap_or_default();
)
.map(|(chunks, _)| chunks)
})?;
// Assemble output
let total_bytes = checked_byte_len(dataspace.checked_num_elements()?, elem_size)?;
@@ -876,10 +906,11 @@ pub fn read_chunked_data_cached(
};
// Chunks stored as-is (no pipeline, or the filter mask says this chunk
// skipped it) are copied straight from the file bytes: they are already in
// memory, so routing them through a Vec and then an aligned cache buffer
// was two extra copies of the whole dataset for nothing.
let stored_raw = |c: &ChunkInfo| pipeline.is_none() || c.filter_mask != 0;
// skipped every filter) are copied straight from the file bytes: they are
// already in memory, so routing them through a Vec and then an aligned
// cache buffer was two extra copies of the whole dataset for nothing.
let stored_raw =
|c: &ChunkInfo| pipeline.is_none_or(|pl| all_filters_skipped(pl, c.filter_mask));
let mut misses: Vec<&ChunkInfo> = Vec::new();
for chunk_info in &chunks {
if stored_raw(chunk_info) {
@@ -887,7 +918,7 @@ pub fn read_chunked_data_cached(
continue;
}
let coord: Vec<u64> = chunk_info.offsets.iter().take(rank).copied().collect();
match cache.get_decompressed_aligned(&coord) {
match cache.get_decompressed_in(addr, &coord) {
Some(cached) => place(&cached, chunk_info),
None => misses.push(chunk_info),
}
@@ -901,7 +932,13 @@ pub fn read_chunked_data_cached(
let cache_them = total_bytes <= cache.max_bytes();
if let Some(pl) = pipeline {
let decode = |c: &&ChunkInfo| -> Result<Vec<u8>, FormatError> {
decompress_chunk(raw_bytes(c)?, pl, chunk_total_bytes, elem_size as u32)
decompress_chunk_masked(
raw_bytes(c)?,
pl,
chunk_total_bytes,
elem_size as u32,
c.filter_mask,
)
};
for batch in misses.chunks(DECODE_BATCH) {
#[cfg(feature = "parallel")]
@@ -918,7 +955,7 @@ pub fn read_chunked_data_cached(
let data = data?;
if cache_them {
let coord: Vec<u64> = chunk_info.offsets.iter().take(rank).copied().collect();
let cached = cache.put_decompressed(coord, data);
let cached = cache.put_decompressed_in(addr, coord, data);
place(&cached, chunk_info);
} else {
place(&data, chunk_info);
@@ -1122,24 +1159,20 @@ pub fn read_chunked_data_sweep(
)));
}
// The per-file cache is shared across datasets; bind it to this one so a
// different dataset's chunk index is never reused for this read.
cache.ensure_dataset(addr);
// Populate chunk index on first access
if !cache.has_index() {
let (chunks, _) = list_chunks(
// The per-file cache is shared across datasets (and threads); every
// lookup is keyed by this dataset's chunk-index address, so another
// dataset's index or chunks are never used for this read.
let chunks = cache.chunks_for(addr, rank, || {
list_chunks(
file_data,
layout,
dataspace,
elem_size,
offset_size,
length_size,
)?;
cache.populate_index(&chunks, rank);
}
let chunks = cache.all_indexed_chunks().unwrap_or_default();
)
.map(|(chunks, _)| chunks)
})?;
// Assemble output
let total_bytes = checked_byte_len(dataspace.checked_num_elements()?, elem_size)?;
@@ -1170,12 +1203,12 @@ pub fn read_chunked_data_sweep(
// Issue prefetch hint for predicted next chunks
if !sweep.predicted_next.is_empty() {
cache.prefetch_hint(&sweep.predicted_next);
cache.prefetch_hint_in(addr, &sweep.predicted_next);
cache.set_sweep_direction(sweep.direction);
}
// Try decompressed cache first
let decompressed = if let Some(cached) = cache.get_decompressed_aligned(&coord) {
let decompressed = if let Some(cached) = cache.get_decompressed_in(addr, &coord) {
cached
} else {
// Decompress from file
@@ -1184,15 +1217,17 @@ pub fn read_chunked_data_sweep(
ensure_len(file_data, c_addr, size)?;
let raw_chunk = &file_data[c_addr..c_addr + size];
let dec = if let Some(pl) = pipeline {
if chunk_info.filter_mask == 0 {
decompress_chunk(raw_chunk, pl, chunk_total_bytes, elem_size as u32)?
} else {
raw_chunk.to_vec()
}
decompress_chunk_masked(
raw_chunk,
pl,
chunk_total_bytes,
elem_size as u32,
chunk_info.filter_mask,
)?
} else {
raw_chunk.to_vec()
};
cache.put_decompressed(coord, dec)
cache.put_decompressed_in(addr, coord, dec)
};
let chunk_offsets: Vec<usize> = chunk_info
@@ -1276,48 +1311,34 @@ pub fn read_chunked_data_indexed(
)));
}
// The per-file cache is shared across datasets; bind it to this one so a
// different dataset's chunk index is never reused for this read.
cache.ensure_dataset(addr);
// Build chunk index on first access
if !cache.has_chunk_index() {
let (chunks, _) = list_chunks(
// Chunk index and assembly plan for this dataset, built on first access
// and kept per dataset (keyed by chunk-index address) in the shared cache.
let plan = cache.chunk_layout_for(
addr,
rank,
|| {
list_chunks(
file_data,
layout,
dataspace,
elem_size,
offset_size,
length_size,
)
.map(|(chunks, _)| chunks)
},
&ds_dims,
&chunk_dims,
elem_size,
)?;
cache.populate_chunk_index(&chunks, rank);
// Also populate the legacy index for compatibility
if !cache.has_index() {
cache.populate_index(&chunks, rank);
}
}
// Build chunk layout on first access
if !cache.has_chunk_layout() {
cache.populate_chunk_layout(&ds_dims, &chunk_dims, elem_size);
}
// Get the layout info (mappings, output size, chunk total bytes)
let (mappings_info, output_bytes, chunk_total_bytes) = cache
.with_chunk_layout(|layout| {
let info: Vec<_> = layout
.mappings
.iter()
.map(|m| (m.coord.clone(), m.file_offset, m.file_size, m.filter_mask))
.collect();
(info, layout.output_bytes, layout.chunk_total_bytes)
})
.ok_or_else(|| FormatError::ChunkedReadError("chunk layout not available".into()))?;
let chunk_total_bytes = plan.chunk_total_bytes;
// Decompress chunks (using LRU cache where possible)
let mut chunk_buffers: Vec<Arc<CacheAlignedBuffer>> = Vec::with_capacity(mappings_info.len());
for (coord, file_offset, file_size, filter_mask) in &mappings_info {
if let Some(cached) = cache.get_decompressed_aligned(coord) {
let mut chunk_buffers: Vec<Arc<CacheAlignedBuffer>> = Vec::with_capacity(plan.mappings.len());
for m in &plan.mappings {
let (coord, file_offset, file_size, filter_mask) =
(&m.coord, &m.file_offset, &m.file_size, &m.filter_mask);
if let Some(cached) = cache.get_decompressed_in(addr, coord) {
chunk_buffers.push(cached);
} else {
let c_addr = *file_offset as usize;
@@ -1325,26 +1346,26 @@ pub fn read_chunked_data_indexed(
ensure_len(file_data, c_addr, size)?;
let raw_chunk = &file_data[c_addr..c_addr + size];
let decompressed = if let Some(pl) = pipeline {
if *filter_mask == 0 {
decompress_chunk(raw_chunk, pl, chunk_total_bytes, elem_size as u32)?
} else {
raw_chunk.to_vec()
}
decompress_chunk_masked(
raw_chunk,
pl,
chunk_total_bytes,
elem_size as u32,
*filter_mask,
)?
} else {
raw_chunk.to_vec()
};
let aligned = CacheAlignedBuffer::from_vec(decompressed);
let arc = cache.put_decompressed_aligned(coord.clone(), aligned);
let arc = cache.put_decompressed_aligned_in(addr, coord.clone(), aligned);
chunk_buffers.push(arc);
}
}
// Assemble using pre-computed layout
let mut output = vec![0u8; output_bytes];
let mut output = vec![0u8; plan.output_bytes];
let data_refs: Vec<&[u8]> = chunk_buffers.iter().map(|b| b.as_slice()).collect();
cache.with_chunk_layout(|layout| {
layout.assemble(&data_refs, &mut output);
});
plan.assemble(&data_refs, &mut output);
Ok(output)
}
@@ -1592,7 +1613,8 @@ mod tests {
} else {
0
};
write_offset(&mut buf, off, offset_size);
// Key offsets are always 8 bytes (they are coordinates).
write_offset(&mut buf, off, 8);
}
// Child: address
write_offset(&mut buf, chunk.address, offset_size);
@@ -1602,7 +1624,7 @@ mod tests {
buf.extend_from_slice(&0u32.to_le_bytes()); // chunk_size
buf.extend_from_slice(&0u32.to_le_bytes()); // filter_mask
for _ in 0..ndims {
write_offset(&mut buf, u64::MAX, offset_size);
write_offset(&mut buf, u64::MAX, 8);
}
buf
@@ -1680,6 +1702,37 @@ mod tests {
assert_eq!(result[2].address, 0x300);
}
#[test]
fn collect_chunks_with_four_byte_addresses() {
// Sibling and child addresses are 4 bytes; the key offsets stay 8.
let ndims = 3;
let os: u8 = 4;
let chunks = vec![
ChunkInfo {
chunk_size: 80,
filter_mask: 2,
offsets: vec![0, 5, 0],
address: 0x1000,
},
ChunkInfo {
chunk_size: 96,
filter_mask: 0,
offsets: vec![8, 10, 0],
address: 0x2000,
},
];
let btree = build_chunk_btree_leaf(&chunks, ndims, os);
assert_eq!(btree.len(), 8 + 2 * 4 + 2 * (8 + 3 * 8 + 4) + (8 + 3 * 8));
let result = collect_chunk_info(&btree, 0, ndims, os, os).unwrap();
assert_eq!(result.len(), 2);
for (got, want) in result.iter().zip(&chunks) {
assert_eq!(got.offsets, want.offsets);
assert_eq!(got.address, want.address);
assert_eq!(got.chunk_size, want.chunk_size);
assert_eq!(got.filter_mask, want.filter_mask);
}
}
#[test]
fn collect_empty_btree() {
let ndims = 2;
@@ -1774,6 +1827,7 @@ mod tests {
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
};
let dataspace = Dataspace {
@@ -1797,6 +1851,7 @@ mod tests {
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
};
let dataspace = Dataspace {
space_type: DataspaceType::Simple,
@@ -1954,6 +2009,7 @@ mod tests {
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
};
let dataspace = Dataspace {
space_type: DataspaceType::Simple,
@@ -2036,6 +2092,7 @@ mod tests {
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
};
let dataspace = Dataspace {
space_type: DataspaceType::Simple,
@@ -2198,6 +2255,7 @@ mod tests {
chunk_index_type: Some(1),
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
};
let dataspace = Dataspace {
space_type: DataspaceType::Simple,
@@ -2227,12 +2285,12 @@ mod tests {
let datatype = make_f64_type();
let cache = ChunkCache::new();
assert!(!cache.has_index());
assert_eq!(cache.indexed_dataset_count(), 0);
let raw = read_chunked_data_cached(
&file_data, &layout, &dataspace, &datatype, None, 8, 8, &cache,
)
.unwrap();
assert!(cache.has_index());
assert_eq!(cache.indexed_dataset_count(), 1);
assert_eq!(raw.len(), 20 * 8);
for i in 0..20 {
let val = f64::from_le_bytes(raw[i * 8..(i + 1) * 8].try_into().unwrap());
@@ -2254,7 +2312,7 @@ mod tests {
&file_data, &layout, &dataspace, &datatype, None, 8, 8, &cache,
)
.unwrap();
assert!(cache.has_index());
assert_eq!(cache.indexed_dataset_count(), 1);
assert_eq!(cache.cached_chunk_count(), 0);
// Second read — reuses the cached index
@@ -2263,6 +2321,7 @@ mod tests {
)
.unwrap();
assert_eq!(raw1, raw2);
assert_eq!(cache.indexed_dataset_count(), 1);
}
#[test]
+400 -101
View File
@@ -4,15 +4,16 @@
extern crate alloc;
#[cfg(not(feature = "std"))]
use alloc::{vec, vec::Vec};
use alloc::{format, vec, vec::Vec};
use crate::checksum::jenkins_lookup3;
use crate::chunk_cache::{CACHE_LINE_SIZE, align_to_cache_line};
use crate::chunk_grid::ChunkGrid;
use crate::ea_writer;
use crate::error::FormatError;
use crate::filter_pipeline::{
FILTER_DEFLATE, FILTER_FLETCHER32, FILTER_LZ4, FILTER_PCODEC, FILTER_SHUFFLE, FILTER_ZSTD,
FilterDescription, FilterPipeline,
FILTER_DEFLATE, FILTER_FLETCHER32, FILTER_LZ4, FILTER_PCODEC, FILTER_PCODEC_NAME,
FILTER_SHUFFLE, FILTER_ZSTD, FilterDescription, FilterPipeline,
};
use crate::filters::compress_chunk;
/// Round a file offset up to the next cache-line boundary.
@@ -44,7 +45,8 @@ pub struct ChunkOptions {
pub lz4: bool,
/// Zstandard compression level (1-22), None = no zstd. Filter ID 32015.
pub zstd_level: Option<u32>,
/// Pcodec lossless numerical compression. Filter ID 32023.
/// Pcodec lossless numerical compression. Private, unregistered filter
/// ID [`FILTER_PCODEC`] (480): only clawhdf5 can read it.
pub pcodec: bool,
}
@@ -115,7 +117,7 @@ impl ChunkOptions {
if self.pcodec {
filters.push(FilterDescription {
filter_id: FILTER_PCODEC,
name: Some("pcodec".into()),
name: Some(FILTER_PCODEC_NAME.into()),
flags: 0,
client_data: vec![element_size],
});
@@ -443,6 +445,27 @@ fn serialize_v4_fixed_array(
element_size: u32,
max_bits: u8,
) -> Vec<u8> {
let mut buf = layout_v4_chunked_prefix(chunk_dims, element_size);
// chunk index type = 3 (Fixed Array)
buf.push(3);
// max_dblk_page_nelmts_bits — must match FAHD max_nelmts_bits
buf.push(max_bits);
// Fixed Array header address
match offset_size {
4 => buf.extend_from_slice(&(fixed_array_address as u32).to_le_bytes()),
8 => buf.extend_from_slice(&fixed_array_address.to_le_bytes()),
_ => {}
}
buf
}
/// The part of a v4 chunked layout message before the chunk index type:
/// version, class, flags and the chunk dimensions (plus the element size).
fn layout_v4_chunked_prefix(chunk_dims: &[u32], element_size: u32) -> Vec<u8> {
let mut buf = Vec::new();
buf.push(4); // version
buf.push(2); // class = chunked
@@ -482,124 +505,142 @@ fn serialize_v4_fixed_array(
4 => buf.extend_from_slice(&element_size.to_le_bytes()),
_ => {}
}
// chunk index type = 3 (Fixed Array)
buf.push(3);
// max_dblk_page_nelmts_bits — must match FAHD max_nelmts_bits
buf.push(max_bits);
// Fixed Array header address
match offset_size {
4 => buf.extend_from_slice(&(fixed_array_address as u32).to_le_bytes()),
8 => buf.extend_from_slice(&fixed_array_address.to_le_bytes()),
_ => {}
}
buf
}
/// log2 of the elements per Fixed Array data block page (the library's
/// default, `H5D_FARRAY_MAX_DBLK_PAGE_NELMTS_BITS`).
const FA_PAGE_BITS: u8 = 10;
pub(crate) fn push_addr(buf: &mut Vec<u8>, addr: u64, offset_size: u8) {
match offset_size {
4 => buf.extend_from_slice(&(addr as u32).to_le_bytes()),
_ => buf.extend_from_slice(&addr.to_le_bytes()),
}
}
/// Width of the chunk-size field of a filtered chunk index element. Must
/// match the library's `H5D_FARRAY_FILT_COMPUTE_CHUNK_SIZE_LEN` (the EA and
/// B-tree v2 indexes use the same formula):
/// `1 + ((log2(unfiltered chunk bytes) + 8) / 8)`, capped at 8.
pub(crate) fn filtered_chunk_size_len(slots: &[Option<WrittenChunk>]) -> usize {
let max_raw = slots
.iter()
.flatten()
.map(|c| c.raw_size)
.max()
.unwrap_or(1);
let log2_val = if max_raw <= 1 {
0
} else {
63 - max_raw.leading_zeros()
};
(1 + ((log2_val + 8) / 8) as usize).min(8)
}
/// Append one chunk index element: the chunk's address, plus its stored size
/// and filter mask when the dataset is filtered. `None` is an unallocated
/// chunk (undefined address, zero size and mask).
pub(crate) fn push_index_element(
buf: &mut Vec<u8>,
slot: Option<&WrittenChunk>,
offset_size: u8,
chunk_size_bytes: Option<usize>,
) {
match slot {
Some(c) => {
push_addr(buf, c.address, offset_size);
if let Some(n) = chunk_size_bytes {
buf.extend_from_slice(&c.compressed_size.to_le_bytes()[..n]);
buf.extend_from_slice(&c.filter_mask.to_le_bytes());
}
}
None => {
buf.extend(core::iter::repeat_n(0xFF, offset_size as usize));
if let Some(n) = chunk_size_bytes {
buf.extend(core::iter::repeat_n(0x00, n + 4));
}
}
}
}
/// Build a complete Fixed Array at a known absolute address.
///
/// `slots` holds one entry per element of the array, i.e. per chunk of the
/// dataset's *maximum* extent in the order [`crate::chunk_grid`] defines;
/// `None` marks a chunk that is not allocated. An array with more elements
/// than fit in one page (`2^FA_PAGE_BITS`) gets a paged data block: a
/// page-init bitmap after the prefix, then one checksummed page per
/// `2^FA_PAGE_BITS` elements, the last one short (`H5FA__dblock_create`).
pub fn build_fixed_array_at(
chunks: &[WrittenChunk],
slots: &[Option<WrittenChunk>],
offset_size: u8,
length_size: u8,
has_filters: bool,
fa_base_address: u64,
) -> Vec<u8> {
let os = offset_size as usize;
let num_elements = chunks.len();
// For filtered chunks, compute chunk_size encoding width.
// Must match the HDF5 C library's H5D_FARRAY_FILT_COMPUTE_CHUNK_SIZE_LEN macro:
// chunk_size_len = 1 + ((H5VM_log2_gen(chunk.size) + 8) / 8)
// where chunk.size is the unfiltered chunk size in bytes (product of all chunk dims).
let chunk_size_bytes: usize = if has_filters {
let max_raw = chunks.iter().map(|c| c.raw_size).max().unwrap_or(1);
let log2_val = if max_raw <= 1 {
0
} else {
63 - max_raw.leading_zeros()
};
let len = 1 + ((log2_val + 8) / 8) as usize;
len.min(8)
} else {
0
};
let elem_size = if has_filters {
os + chunk_size_bytes + 4
} else {
os
};
let num_elements = slots.len();
let chunk_size_bytes = has_filters.then(|| filtered_chunk_size_len(slots));
let elem_size = os + chunk_size_bytes.map_or(0, |n| n + 4);
let client_id: u8 = if has_filters { 1 } else { 0 };
// FAHD total size
let nelmts_field_size = length_size as usize;
let fahd_total_size = 4 + 1 + 1 + 1 + 1 + nelmts_field_size + os + 4;
let fahd_total_size = 4 + 1 + 1 + 1 + 1 + length_size as usize + os + 4;
let fadb_address = fa_base_address + fahd_total_size as u64;
// Build FAHD
let mut fahd = Vec::with_capacity(fahd_total_size);
fahd.extend_from_slice(b"FAHD");
fahd.push(0); // version
fahd.push(client_id);
fahd.push(elem_size as u8);
// max_nelmts_bits: use 10 as default (page_size = 1024), matching h5py convention
let max_bits: u8 = 10;
fahd.push(max_bits);
fahd.push(FA_PAGE_BITS);
match length_size {
4 => fahd.extend_from_slice(&(num_elements as u32).to_le_bytes()),
8 => fahd.extend_from_slice(&(num_elements as u64).to_le_bytes()),
_ => fahd.extend_from_slice(&(num_elements as u64).to_le_bytes()),
}
match offset_size {
4 => fahd.extend_from_slice(&(fadb_address as u32).to_le_bytes()),
8 => fahd.extend_from_slice(&fadb_address.to_le_bytes()),
_ => fahd.extend_from_slice(&fadb_address.to_le_bytes()),
}
// Checksum
push_addr(&mut fahd, fadb_address, offset_size);
let checksum = jenkins_lookup3(&fahd);
fahd.extend_from_slice(&checksum.to_le_bytes());
assert_eq!(fahd.len(), fahd_total_size);
// Build FADB
// FADB prefix
let mut fadb = Vec::new();
fadb.extend_from_slice(b"FADB");
fadb.push(0); // version
fadb.push(client_id);
push_addr(&mut fadb, fa_base_address, offset_size);
// header address
match offset_size {
4 => fadb.extend_from_slice(&(fa_base_address as u32).to_le_bytes()),
8 => fadb.extend_from_slice(&fa_base_address.to_le_bytes()),
_ => fadb.extend_from_slice(&fa_base_address.to_le_bytes()),
let page_nelmts = 1usize << FA_PAGE_BITS;
if num_elements <= page_nelmts {
// Unpaged: the elements follow the prefix, one checksum over both.
for slot in slots {
push_index_element(&mut fadb, slot.as_ref(), offset_size, chunk_size_bytes);
}
// Element data
for chunk in chunks {
match offset_size {
4 => fadb.extend_from_slice(&(chunk.address as u32).to_le_bytes()),
8 => fadb.extend_from_slice(&chunk.address.to_le_bytes()),
_ => fadb.extend_from_slice(&chunk.address.to_le_bytes()),
}
if has_filters {
// Write compressed size using chunk_size_bytes (variable width)
let cs_bytes = chunk.compressed_size.to_le_bytes();
fadb.extend_from_slice(&cs_bytes[..chunk_size_bytes]);
fadb.extend_from_slice(&chunk.filter_mask.to_le_bytes());
}
}
// FADB checksum
let fadb_checksum = jenkins_lookup3(&fadb);
fadb.extend_from_slice(&fadb_checksum.to_le_bytes());
} else {
// Paged: every page is written, so every page-init bit is set
// (MSB-first, as `H5VM_bit_set` packs them). The prefix and bitmap
// share a checksum; each page carries its own.
let npages = num_elements.div_ceil(page_nelmts);
let mut bitmap = vec![0u8; npages.div_ceil(8)];
for p in 0..npages {
bitmap[p / 8] |= 0x80 >> (p % 8);
}
fadb.extend_from_slice(&bitmap);
let prefix_checksum = jenkins_lookup3(&fadb);
fadb.extend_from_slice(&prefix_checksum.to_le_bytes());
for page in slots.chunks(page_nelmts) {
let start = fadb.len();
for slot in page {
push_index_element(&mut fadb, slot.as_ref(), offset_size, chunk_size_bytes);
}
let page_checksum = jenkins_lookup3(&fadb[start..]);
fadb.extend_from_slice(&page_checksum.to_le_bytes());
}
}
let mut combined = fahd;
combined.extend_from_slice(&fadb);
@@ -667,7 +708,8 @@ pub fn build_chunked_data_from_precompressed(
pre: &PrecompressedChunks,
base_address: u64,
maxshape: Option<&[u64]>,
) -> ChunkedDataResult {
) -> Result<ChunkedDataResult, FormatError> {
let index = ChunkIndexPlan::new(&pre.shape, maxshape, &pre.chunk_dims)?;
let offset_size: u8 = 8;
let length_size: u8 = 8;
let num_chunks = pre.chunks.len();
@@ -693,17 +735,18 @@ pub fn build_chunked_data_from_precompressed(
}
let chunk_dims_u32: Vec<u32> = pre.chunk_dims.iter().map(|&d| d as u32).collect();
let use_extensible = maxshape.is_some_and(|ms| ms.contains(&u64::MAX));
let aligned_idx = align_to_cache_line(data_buf.len());
if aligned_idx > data_buf.len() {
data_buf.resize(aligned_idx, 0u8);
}
let layout_message = if use_extensible {
let layout_message = match &index {
ChunkIndexPlan::ExtensibleArray(grid) => {
let ea_address = base_address + data_buf.len() as u64;
let slots = index_slots(grid, &pre.shape, &pre.chunk_dims, &written_chunks, None)?;
let ea_bytes = ea_writer::build_extensible_array_at(
&written_chunks,
&slots,
offset_size,
length_size,
pre.has_filters,
@@ -716,7 +759,8 @@ pub fn build_chunked_data_from_precompressed(
offset_size,
element_size as u32,
)
} else if num_chunks == 1 {
}
ChunkIndexPlan::SingleChunk => {
let chunk_addr = written_chunks[0].address;
let filtered_size = if pre.has_filters {
Some(written_chunks[0].compressed_size)
@@ -732,10 +776,18 @@ pub fn build_chunked_data_from_precompressed(
offset_size,
element_size as u32,
)
} else {
}
ChunkIndexPlan::FixedArray(grid, nslots) => {
let fa_address = base_address + data_buf.len() as u64;
let fa_bytes = build_fixed_array_at(
let slots = index_slots(
grid,
&pre.shape,
&pre.chunk_dims,
&written_chunks,
Some(*nslots),
)?;
let fa_bytes = build_fixed_array_at(
&slots,
offset_size,
length_size,
pre.has_filters,
@@ -747,15 +799,263 @@ pub fn build_chunked_data_from_precompressed(
fa_address,
offset_size,
element_size as u32,
10, // max_nelmts_bits — matches h5py convention
FA_PAGE_BITS,
)
}
ChunkIndexPlan::BTreeV2 => {
let bt_address = base_address + data_buf.len() as u64;
let records: Vec<(Vec<u64>, &WrittenChunk)> = written_chunks
.iter()
.enumerate()
.map(|(i, c)| (scaled_coords(&pre.shape, &pre.chunk_dims, i), c))
.collect();
let (bt_bytes, node_size) = build_btree_v2_chunk_index_at(
pre.shape.len(),
&records,
offset_size,
length_size,
pre.has_filters,
bt_address,
)?;
data_buf.extend_from_slice(&bt_bytes);
serialize_v4_btree_v2(
&chunk_dims_u32,
bt_address,
offset_size,
element_size as u32,
node_size,
)
}
};
ChunkedDataResult {
Ok(ChunkedDataResult {
data_bytes: data_buf,
layout_message,
pipeline_message: pre.pipeline_message.clone(),
})
}
/// Most slots a Fixed Array index may have before we refuse to build it: its
/// data block holds one element per chunk of the *maximum* extent, so a huge
/// finite maxshape with small chunks would otherwise exhaust memory.
const MAX_FIXED_ARRAY_SLOTS: u64 = 1 << 26;
/// Which chunk index a dataset gets, following the library's choice in
/// `H5D__layout_set_latest_indexing`: version-2 B-tree for more than one
/// unlimited dimension, Extensible Array for exactly one, Fixed Array for a
/// finite maxshape, Single Chunk when the whole maximum extent is one chunk.
enum ChunkIndexPlan {
SingleChunk,
/// The grid and the number of array elements (chunks of the max extent).
FixedArray(ChunkGrid, usize),
ExtensibleArray(ChunkGrid),
BTreeV2,
}
impl ChunkIndexPlan {
fn new(
shape: &[u64],
maxshape: Option<&[u64]>,
chunk_dims: &[u64],
) -> Result<Self, FormatError> {
let bad = |what: &str| FormatError::ChunkedReadError(format!("maxshape: {what}"));
if let Some(ms) = maxshape {
if ms.len() != shape.len() {
return Err(bad("rank differs from the shape"));
}
if ms.iter().zip(shape).any(|(&m, &s)| m < s) {
return Err(bad("smaller than the shape"));
}
}
let max = maxshape.unwrap_or(shape);
let nunlim = max.iter().filter(|&&d| d == u64::MAX).count();
match nunlim {
0 => {
let nslots = max
.iter()
.zip(chunk_dims)
.try_fold(1u64, |acc, (&m, &c)| acc.checked_mul(m.div_ceil(c.max(1))))
.filter(|&n| n <= MAX_FIXED_ARRAY_SLOTS)
.ok_or_else(|| {
bad("too many chunks for a Fixed Array index; \
use larger chunks or an unlimited dimension")
})?;
// A Single Chunk index needs that one chunk to exist; an
// empty dataset gets an all-unallocated Fixed Array instead.
let empty = shape.contains(&0);
if nslots == 1 && !empty {
Ok(Self::SingleChunk)
} else {
let grid = ChunkGrid::fixed_array(shape, Some(max), chunk_dims)?;
Ok(Self::FixedArray(grid, nslots as usize))
}
}
1 => Ok(Self::ExtensibleArray(ChunkGrid::extensible_array(
shape,
Some(max),
chunk_dims,
)?)),
_ => Ok(Self::BTreeV2),
}
}
}
/// Place each written chunk at its linear index in `grid`. `chunks` are in
/// row-major order over the chunks of the current extent (`split_into_chunks`).
/// `len` fixes the slot count (Fixed Array); otherwise it is one past the
/// highest index used.
fn index_slots(
grid: &ChunkGrid,
shape: &[u64],
chunk_dims: &[u64],
chunks: &[WrittenChunk],
len: Option<usize>,
) -> Result<Vec<Option<WrittenChunk>>, FormatError> {
let mut placed: Vec<(usize, &WrittenChunk)> = Vec::with_capacity(chunks.len());
for (i, chunk) in chunks.iter().enumerate() {
let scaled = scaled_coords(shape, chunk_dims, i);
let idx = usize::try_from(grid.linear_index(&scaled))
.map_err(|_| FormatError::Overflow("chunk index slot".into()))?;
placed.push((idx, chunk));
}
let n = len.unwrap_or_else(|| placed.iter().map(|&(i, _)| i + 1).max().unwrap_or(0));
let mut slots = vec![None; n];
for (idx, chunk) in placed {
*slots
.get_mut(idx)
.ok_or_else(|| FormatError::Overflow("chunk index slot".into()))? = Some(chunk.clone());
}
Ok(slots)
}
/// Scaled coordinates (`offset / chunk_dim`) of the `i`-th chunk in the
/// row-major order `split_into_chunks` produces over the current extent.
fn scaled_coords(shape: &[u64], chunk_dims: &[u64], i: usize) -> Vec<u64> {
let rank = shape.len();
let mut scaled = vec![0u64; rank];
let mut rem = i as u64;
for d in (0..rank).rev() {
let n = shape[d].div_ceil(chunk_dims[d]);
scaled[d] = rem % n;
rem /= n;
}
scaled
}
/// Node size the library gives a chunk index B-tree (`H5D_BT2_NODE_SIZE`),
/// with its split and merge percentages.
const BT2_NODE_SIZE: u32 = 2048;
const BT2_SPLIT_PERCENT: u8 = 100;
const BT2_MERGE_PERCENT: u8 = 40;
/// B-tree v2 record types for chunk indexes (`H5B2_CDSET_ID`,
/// `H5B2_CDSET_FILT_ID`).
const BT2_CHUNK_UNFILTERED: u8 = 10;
const BT2_CHUNK_FILTERED: u8 = 11;
/// Build a version-2 B-tree chunk index (the library's index for datasets
/// with more than one unlimited dimension) at a known absolute address.
///
/// `records` are `(scaled coordinates, chunk)` in lexicographic order of the
/// coordinates, which is the order the library's comparator
/// (`H5VM_vector_cmp_u`) keeps them in. The tree is a single leaf: the
/// library's 2048-byte node when the records fit, otherwise a leaf node
/// sized to hold them all (the root's record count is 16-bit, so at most
/// 65535 chunks). Returns the bytes and the node size the layout message
/// must record.
fn build_btree_v2_chunk_index_at(
rank: usize,
records: &[(Vec<u64>, &WrittenChunk)],
offset_size: u8,
length_size: u8,
has_filters: bool,
base_address: u64,
) -> Result<(Vec<u8>, u32), FormatError> {
let os = offset_size as usize;
let nrec = u16::try_from(records.len()).map_err(|_| {
FormatError::ChunkedReadError(
"more than 65535 chunks with more than one unlimited dimension: \
use larger chunks"
.into(),
)
})?;
let chunk_size_bytes = has_filters.then(|| {
let slots: Vec<Option<WrittenChunk>> =
records.iter().map(|(_, c)| Some((*c).clone())).collect();
filtered_chunk_size_len(&slots)
});
let record_size = os + chunk_size_bytes.map_or(0, |n| n + 4) + 8 * rank;
// Leaf: signature, version, type, records, checksum.
let leaf_len = 4 + 1 + 1 + records.len() * record_size + 4;
let node_size = u32::try_from(leaf_len)
.map_err(|_| FormatError::Overflow("B-tree v2 leaf size".into()))?
.max(BT2_NODE_SIZE);
let tree_type = if has_filters {
BT2_CHUNK_FILTERED
} else {
BT2_CHUNK_UNFILTERED
};
let hdr_len = 4 + 1 + 1 + 4 + 2 + 2 + 1 + 1 + os + 2 + length_size as usize + 4;
let leaf_address = base_address + hdr_len as u64;
let mut out = Vec::with_capacity(hdr_len + node_size as usize);
out.extend_from_slice(b"BTHD");
out.push(0); // version
out.push(tree_type);
out.extend_from_slice(&node_size.to_le_bytes());
out.extend_from_slice(&(record_size as u16).to_le_bytes());
out.extend_from_slice(&0u16.to_le_bytes()); // depth
out.push(BT2_SPLIT_PERCENT);
out.push(BT2_MERGE_PERCENT);
if records.is_empty() {
out.extend(core::iter::repeat_n(0xFF, os));
} else {
push_addr(&mut out, leaf_address, offset_size);
}
out.extend_from_slice(&nrec.to_le_bytes());
match length_size {
4 => out.extend_from_slice(&(records.len() as u32).to_le_bytes()),
_ => out.extend_from_slice(&(records.len() as u64).to_le_bytes()),
}
let sum = jenkins_lookup3(&out);
out.extend_from_slice(&sum.to_le_bytes());
debug_assert_eq!(out.len(), hdr_len);
if records.is_empty() {
return Ok((out, node_size));
}
let leaf_start = out.len();
out.extend_from_slice(b"BTLF");
out.push(0); // version
out.push(tree_type);
for (scaled, chunk) in records {
push_index_element(&mut out, Some(chunk), offset_size, chunk_size_bytes);
for &c in scaled {
out.extend_from_slice(&c.to_le_bytes());
}
}
let sum = jenkins_lookup3(&out[leaf_start..]);
out.extend_from_slice(&sum.to_le_bytes());
// The library reads whole nodes; pad the leaf out to the node size.
out.resize(leaf_start + node_size as usize, 0);
Ok((out, node_size))
}
/// Serialize a v4 layout message for a version-2 B-tree chunk index.
fn serialize_v4_btree_v2(
chunk_dims: &[u32],
btree_address: u64,
offset_size: u8,
element_size: u32,
node_size: u32,
) -> Vec<u8> {
let mut buf = layout_v4_chunked_prefix(chunk_dims, element_size);
buf.push(5); // chunk index type = 5 (version-2 B-tree)
buf.extend_from_slice(&node_size.to_le_bytes());
buf.push(BT2_SPLIT_PERCENT);
buf.push(BT2_MERGE_PERCENT);
push_addr(&mut buf, btree_address, offset_size);
buf
}
/// Build chunked data with absolute addresses.
@@ -790,11 +1090,7 @@ pub fn build_chunked_data_at_ext(
maxshape: Option<&[u64]>,
) -> Result<ChunkedDataResult, FormatError> {
let pre = precompress_chunks(raw_data, shape, chunk_dims, element_size, options)?;
Ok(build_chunked_data_from_precompressed(
&pre,
base_address,
maxshape,
))
build_chunked_data_from_precompressed(&pre, base_address, maxshape)
}
/// Write selected elements into an existing in-memory dataset buffer.
@@ -1314,6 +1610,7 @@ mod tests {
chunk_index_type,
single_chunk_filtered_size,
single_chunk_filter_mask,
..
} => {
assert_eq!(version, 4);
assert_eq!(chunk_index_type, Some(1));
@@ -1382,7 +1679,8 @@ mod tests {
filter_mask: 0,
},
];
let fa = build_fixed_array_at(&chunks, 8, 8, false, 0x2000);
let slots: Vec<_> = chunks.into_iter().map(Some).collect();
let fa = build_fixed_array_at(&slots, 8, 8, false, 0x2000);
// Should start with FAHD
assert_eq!(&fa[0..4], b"FAHD");
// FAHD size = 4+1+1+1+1+8+8+4 = 28
@@ -1429,7 +1727,8 @@ mod tests {
filter_mask: 0,
},
];
let ea = ea_writer::build_extensible_array_at(&chunks, 8, 8, false, 0x2000);
let slots: Vec<_> = chunks.into_iter().map(Some).collect();
let ea = ea_writer::build_extensible_array_at(&slots, 8, 8, false, 0x2000);
assert_eq!(&ea[0..4], b"EAHD");
// Find EAIB after EAHD: 12 fixed + 6*8 stats + 8 addr + 4 checksum = 72
let aehd_size = 4 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 6 * 8 + 8 + 4;
+342 -14
View File
@@ -1,7 +1,7 @@
//! HDF5 Data Layout message parsing (message type 0x0008).
#[cfg(not(feature = "std"))]
use alloc::{string::String, vec::Vec};
use alloc::{format, string::String, vec::Vec};
#[cfg(feature = "std")]
use std::string::String;
@@ -45,7 +45,9 @@ pub enum DataLayout {
chunk_dimensions: Vec<u32>,
/// B-tree address, or `None` if undefined.
btree_address: Option<u64>,
/// Layout version (3 or 4).
/// Layout version (3 or 4). Version 1/2 messages (HDF5 1.4/1.6-era)
/// use the same version-1 B-tree chunk index as version 3 and are
/// reported as 3.
version: u8,
/// Chunk index type (v4 only).
chunk_index_type: Option<u8>,
@@ -53,6 +55,11 @@ pub enum DataLayout {
single_chunk_filtered_size: Option<u64>,
/// Filter mask for v4 single chunk with filters.
single_chunk_filter_mask: Option<u32>,
/// Layout v4 flag bit 0 (`H5D_CHUNK_DONT_FILTER_PARTIAL_CHUNKS`):
/// partial edge chunks — those extending past the dataset's current
/// extent in some dimension — are stored without the filter pipeline,
/// even though their filter mask is 0. Always `false` for v3.
dont_filter_partial_edge_chunks: bool,
},
/// Virtual dataset layout (v4 only).
Virtual {
@@ -67,21 +74,33 @@ pub enum DataLayout {
},
}
/// Version-1 VDS mapping flag: the source file name is stored by an earlier
/// entry, whose index follows in place of the name.
const VDS_SOURCE_FILE_SHARED: u8 = 0x01;
/// Version-1 VDS mapping flag: likewise for the source dataset name.
const VDS_SOURCE_DSET_SHARED: u8 = 0x02;
/// Version-1 VDS mapping flag: the source is in the virtual file itself
/// (`"."`); no file name is stored.
const VDS_SOURCE_SAME_FILE: u8 = 0x04;
const VDS_ALL_FLAGS: u8 = VDS_SOURCE_FILE_SHARED | VDS_SOURCE_DSET_SHARED | VDS_SOURCE_SAME_FILE;
/// Parse VDS mappings from global-heap object data.
///
/// The global-heap block holding a VDS mapping list is laid out as
/// (reverse-engineered and validated against HDF5 2.0):
/// (`H5D__virtual_store_layout` / `H5D__virtual_load_layout` in libhdf5):
///
/// ```text
/// version(1) · nused(length_size, LE) · entry[nused] · checksum(4)
/// ```
///
/// Each entry is:
/// - source file name — a null-terminated string in **block version 0**; in
/// **block version 1** a same-file reference is encoded as a single `0x04`
/// marker byte (the source file is the virtual file itself) in place of the
/// name;
/// - source dataset name (null-terminated string);
/// - **block version 1 only:** a flags byte. `0x04`: the source is in the
/// virtual file itself and no file name is stored; `0x01`/`0x02`: the
/// source file/dataset name is that of an earlier entry, whose index
/// (`length_size` bytes) is stored instead of the name. libhdf5 2.0 writes
/// version 1 when the file's low version bound is 2.0 and it saves space;
/// - source file name (null-terminated string, unless flagged above);
/// - source dataset name (null-terminated string, unless flagged above);
/// - source selection (serialized `H5S` dataspace selection — self-describing
/// in length);
/// - virtual selection (serialized `H5S` dataspace selection).
@@ -107,7 +126,7 @@ pub fn parse_vds_mappings(
// `nused` is untrusted; don't pre-allocate from it. Each entry consumes at
// least a few bytes, so the loop is naturally bounded by the heap data and
// a bogus `nused` simply errors out on the first short read.
let mut mappings = Vec::new();
let mut mappings: Vec<VdsMapping> = Vec::new();
// Reads one self-describing selection at `pos`, returning its raw bytes and
// advancing past it — bounds-checked so a corrupt selection can't overrun.
let read_selection = |heap_data: &[u8], pos: &mut usize| -> Result<Vec<u8>, FormatError> {
@@ -127,17 +146,57 @@ pub fn parse_vds_mappings(
Ok(bytes)
};
for _ in 0..nused {
// Source file name (with the version-1 same-file marker handled).
let source_file = if version >= 1 && heap_data.get(pos) == Some(&0x04) {
if version > 1 {
return Err(FormatError::ChunkedReadError(
"unsupported VDS mapping block version".into(),
));
}
for i in 0..nused {
// Version 1 prefixes each entry with a flags byte; a name may then be
// omitted (same file) or replaced by the index of an earlier entry
// holding the same name (`H5D__virtual_load_layout`).
let flags = if version >= 1 {
let f = *heap_data.get(pos).ok_or(FormatError::UnexpectedEof {
expected: pos + 1,
available: heap_data.len(),
})?;
pos += 1;
if f & !VDS_ALL_FLAGS != 0 {
return Err(FormatError::ChunkedReadError(
"unknown VDS mapping flags".into(),
));
}
f
} else {
0
};
// Index of an earlier entry, for a shared name.
let earlier = |pos: &mut usize| -> Result<usize, FormatError> {
let idx = read_length(heap_data, *pos, length_size)?;
*pos += ls;
if idx >= i {
return Err(FormatError::ChunkedReadError(
"VDS mapping shares a name with a later entry".into(),
));
}
Ok(idx as usize)
};
let source_file = if flags & VDS_SOURCE_SAME_FILE != 0 {
String::from(".")
} else if flags & VDS_SOURCE_FILE_SHARED != 0 {
let idx = earlier(&mut pos)?;
mappings[idx].source_file.clone()
} else {
read_null_terminated_string(heap_data, &mut pos)?
};
// Source dataset name.
let source_dataset = read_null_terminated_string(heap_data, &mut pos)?;
let source_dataset = if flags & VDS_SOURCE_DSET_SHARED != 0 {
let idx = earlier(&mut pos)?;
mappings[idx].source_dataset.clone()
} else {
read_null_terminated_string(heap_data, &mut pos)?
};
// Source selection, then virtual selection (both self-describing length).
let source_selection = read_selection(heap_data, &mut pos)?;
@@ -256,6 +315,7 @@ impl DataLayout {
let layout_class = data[1];
match version {
1 | 2 => Self::parse_v1_v2(data, offset_size),
3 => Self::parse_v3(data, layout_class, offset_size, length_size),
// v5 (emitted by HDF5 1.14+/2.0 with `libver=latest`) uses the same
// message structure as v4 — only the version number was bumped.
@@ -264,6 +324,87 @@ impl DataLayout {
}
}
/// Layout message versions 1 and 2 (HDF5 before 1.6.3):
///
/// ```text
/// version(1) · dimensionality(1) · layout class(1) · reserved(5)
/// · address(offset_size) — contiguous and chunked only
/// · dimension sizes(4 × dimensionality)
/// · compact data size(4) · compact raw data — compact only
/// ```
///
/// The dimension sizes are the dataset's (contiguous/compact) or the
/// chunk's (chunked) extent plus a trailing element-size dimension, as in
/// version 3's chunked form. libhdf5 ignores them for contiguous storage
/// and sizes the data from the dataspace; the product of the stored
/// dimensions is that same size, and a disagreement (a dimension that was
/// truncated to 32 bits) is caught by the reader's size check rather than
/// returning wrong data.
fn parse_v1_v2(data: &[u8], offset_size: u8) -> Result<DataLayout, FormatError> {
ensure_len(data, 0, 8)?;
let dimensionality = data[1] as usize;
let layout_class = data[2];
// H5O_LAYOUT_NDIMS: 32 dataspace dimensions + the element-size one.
if dimensionality > 33 {
return Err(FormatError::Overflow(format!(
"data layout dimensionality {dimensionality} exceeds 33"
)));
}
let mut p = 8;
let os = offset_size as usize;
let address = match layout_class {
1 | 2 => {
ensure_len(data, p, os)?;
let a = if is_undefined(data, p, offset_size) {
None
} else {
Some(read_offset(data, p, offset_size)?)
};
p += os;
a
}
0 => None,
_ => return Err(FormatError::InvalidLayoutClass(layout_class)),
};
ensure_len(data, p, dimensionality * 4)?;
let dims: Vec<u32> = data[p..p + dimensionality * 4]
.as_chunks::<4>()
.0
.iter()
.map(|c| u32::from_le_bytes(*c))
.collect();
p += dimensionality * 4;
match layout_class {
0 => {
ensure_len(data, p, 4)?;
let size =
u32::from_le_bytes([data[p], data[p + 1], data[p + 2], data[p + 3]]) as usize;
ensure_len(data, p + 4, size)?;
Ok(DataLayout::Compact {
data: data[p + 4..p + 4 + size].to_vec(),
})
}
1 => {
let size = dims
.iter()
.try_fold(1u64, |acc, &d| acc.checked_mul(d as u64))
.ok_or_else(|| {
FormatError::Overflow(format!("contiguous layout size {dims:?}"))
})?;
Ok(DataLayout::Contiguous { address, size })
}
_ => Ok(DataLayout::Chunked {
chunk_dimensions: dims,
btree_address: address,
version: 3,
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
}),
}
}
fn parse_v3(
data: &[u8],
layout_class: u8,
@@ -322,6 +463,7 @@ impl DataLayout {
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
})
}
_ => Err(FormatError::InvalidLayoutClass(layout_class)),
@@ -505,6 +647,7 @@ impl DataLayout {
chunk_index_type: Some(chunk_index_type),
single_chunk_filtered_size,
single_chunk_filter_mask,
dont_filter_partial_edge_chunks: flags & 0x01 != 0,
})
}
3 => {
@@ -539,6 +682,108 @@ impl DataLayout {
mod tests {
use super::*;
/// Version 1/2 header: version, dimensionality, class, reserved(5).
fn v1v2_header(version: u8, ndims: u8, class: u8) -> Vec<u8> {
vec![version, ndims, class, 0, 0, 0, 0, 0]
}
#[test]
fn v2_compact() {
let mut buf = v1v2_header(2, 2, 0);
// dims (3 elements of 2 bytes) — no address for compact
buf.extend_from_slice(&3u32.to_le_bytes());
buf.extend_from_slice(&2u32.to_le_bytes());
buf.extend_from_slice(&6u32.to_le_bytes()); // compact size (u32 in v1/v2)
buf.extend_from_slice(&[1, 0, 2, 0, 3, 0]);
assert_eq!(
DataLayout::parse(&buf, 8, 8).unwrap(),
DataLayout::Compact {
data: vec![1, 0, 2, 0, 3, 0]
}
);
}
#[test]
fn v1_contiguous_size_from_dimensions() {
let mut buf = v1v2_header(1, 3, 1);
buf.extend_from_slice(&0x800u32.to_le_bytes()); // 4-byte address
for d in [10u32, 20, 4] {
buf.extend_from_slice(&d.to_le_bytes());
}
assert_eq!(
DataLayout::parse(&buf, 4, 4).unwrap(),
DataLayout::Contiguous {
address: Some(0x800),
size: 800,
}
);
}
#[test]
fn v1_contiguous_undefined_address() {
let mut buf = v1v2_header(1, 2, 1);
buf.extend_from_slice(&[0xFF; 8]);
buf.extend_from_slice(&5u32.to_le_bytes());
buf.extend_from_slice(&8u32.to_le_bytes());
assert_eq!(
DataLayout::parse(&buf, 8, 8).unwrap(),
DataLayout::Contiguous {
address: None,
size: 40,
}
);
}
#[test]
fn v1_chunked_maps_to_btree_v1_index() {
let mut buf = v1v2_header(1, 3, 2);
buf.extend_from_slice(&0x1234u64.to_le_bytes());
for d in [50u32, 50, 4] {
buf.extend_from_slice(&d.to_le_bytes());
}
assert_eq!(
DataLayout::parse(&buf, 8, 8).unwrap(),
DataLayout::Chunked {
chunk_dimensions: vec![50, 50, 4],
btree_address: Some(0x1234),
version: 3,
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
}
);
}
#[test]
fn v1v2_rejects_bad_class_dimensionality_and_truncation() {
assert_eq!(
DataLayout::parse(&v1v2_header(1, 1, 3), 8, 8).unwrap_err(),
FormatError::InvalidLayoutClass(3)
);
assert!(matches!(
DataLayout::parse(&v1v2_header(2, 34, 1), 8, 8).unwrap_err(),
FormatError::Overflow(_)
));
// Chunked, dims cut short.
let mut buf = v1v2_header(1, 2, 2);
buf.extend_from_slice(&0x10u64.to_le_bytes());
buf.extend_from_slice(&7u32.to_le_bytes());
assert!(matches!(
DataLayout::parse(&buf, 8, 8).unwrap_err(),
FormatError::UnexpectedEof { .. }
));
// Compact, raw data shorter than its declared size.
let mut buf = v1v2_header(2, 1, 0);
buf.extend_from_slice(&4u32.to_le_bytes());
buf.extend_from_slice(&100u32.to_le_bytes());
buf.extend_from_slice(&[0; 4]);
assert!(matches!(
DataLayout::parse(&buf, 8, 8).unwrap_err(),
FormatError::UnexpectedEof { .. }
));
}
#[test]
fn v3_compact() {
let mut buf = vec![3u8, 0]; // version=3, class=0 (compact)
@@ -602,6 +847,7 @@ mod tests {
chunk_index_type: None,
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
}
);
}
@@ -679,10 +925,35 @@ mod tests {
chunk_index_type: Some(1),
single_chunk_filtered_size: None,
single_chunk_filter_mask: None,
dont_filter_partial_edge_chunks: false,
}
);
}
#[test]
fn v4_chunked_dont_filter_partial_edge_chunks_flag() {
let mut buf = vec![4u8, 2]; // version=4, class=2
buf.push(0x01); // flags bit 0 = don't filter partial edge chunks
buf.push(2); // dimensionality=2
buf.push(4); // dim_size_encoded_length=4
buf.extend_from_slice(&5u32.to_le_bytes());
buf.extend_from_slice(&4u32.to_le_bytes());
buf.push(3); // Fixed Array
buf.push(10); // max_dblk_page_nelmts_bits
buf.extend_from_slice(&0x3000u64.to_le_bytes());
match DataLayout::parse(&buf, 8, 8).unwrap() {
DataLayout::Chunked {
dont_filter_partial_edge_chunks,
btree_address,
..
} => {
assert!(dont_filter_partial_edge_chunks);
assert_eq!(btree_address, Some(0x3000));
}
other => panic!("expected Chunked, got {other:?}"),
}
}
#[test]
fn v4_chunked_single_chunk_with_filters() {
let mut buf = vec![4u8, 2]; // version=4, class=2
@@ -705,6 +976,7 @@ mod tests {
chunk_index_type: Some(1),
single_chunk_filtered_size: Some(1024),
single_chunk_filter_mask: Some(0),
dont_filter_partial_edge_chunks: false,
}
);
}
@@ -815,6 +1087,62 @@ mod tests {
assert_eq!(v1.iter_linear_1d(8).unwrap(), vec![4, 5, 6, 7]);
}
#[test]
fn parse_vds_mappings_v1_shared_names() {
// Written by HDF5 2.0 (h5py, libver=("v200", "v200")) for three
// mappings from `a_rather_long_source_file.h5:a_rather_long_dataset_name`
// and one from the same file: the entries carry flags 0x00, 0x03, 0x03
// and 0x06, so names after the first are stored as entry indices.
let blob: &[u8] = &[
0x01, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x61, 0x5f, 0x72, 0x61,
0x74, 0x68, 0x65, 0x72, 0x5f, 0x6c, 0x6f, 0x6e, 0x67, 0x5f, 0x73, 0x6f, 0x75, 0x72,
0x63, 0x65, 0x5f, 0x66, 0x69, 0x6c, 0x65, 0x2e, 0x68, 0x35, 0x00, 0x61, 0x5f, 0x72,
0x61, 0x74, 0x68, 0x65, 0x72, 0x5f, 0x6c, 0x6f, 0x6e, 0x67, 0x5f, 0x64, 0x61, 0x74,
0x61, 0x73, 0x65, 0x74, 0x5f, 0x6e, 0x61, 0x6d, 0x65, 0x00, 0x02, 0x00, 0x00, 0x00,
0x03, 0x00, 0x00, 0x00, 0x01, 0x02, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
0x01, 0x00, 0x04, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x02,
0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x01, 0x00, 0x01, 0x00, 0x00, 0x00,
0x01, 0x00, 0x01, 0x00, 0x04, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03,
0x00, 0x00, 0x00, 0x01, 0x02, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x01, 0x00, 0x01,
0x00, 0x04, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x02, 0x02,
0x00, 0x00, 0x00, 0x01, 0x00, 0x01, 0x00, 0x01, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01,
0x00, 0x01, 0x00, 0x04, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00,
0x00, 0x00, 0x01, 0x02, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x01, 0x00, 0x01, 0x00,
0x04, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x02, 0x02, 0x00,
0x00, 0x00, 0x02, 0x00, 0x01, 0x00, 0x01, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00,
0x01, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x02, 0x01, 0x00, 0x00, 0x00, 0x00,
0x00, 0x01, 0x00, 0x01, 0x00, 0x04, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00,
0x00, 0x01, 0x02, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x01, 0x00, 0x01, 0x00, 0x01,
0x00, 0x00, 0x00, 0x01, 0x00, 0x01, 0x00, 0x04, 0x00, 0x8e, 0xa7, 0xea, 0x7a,
];
let mappings = parse_vds_mappings(blob, 8).unwrap();
let names: Vec<(&str, &str)> = mappings
.iter()
.map(|m| (m.source_file.as_str(), m.source_dataset.as_str()))
.collect();
let (file, dset) = ("a_rather_long_source_file.h5", "a_rather_long_dataset_name");
assert_eq!(
names,
vec![(file, dset), (file, dset), (file, dset), (".", dset)]
);
}
#[test]
fn parse_vds_mappings_v1_forward_reference_is_error() {
// Entry 0 claiming to share entry 0's file name must not index past
// the entries decoded so far.
let mut blob = vec![0x01u8, 1, 0, 0, 0, 0, 0, 0, 0, 0x01];
blob.extend_from_slice(&[0u8; 8]);
blob.extend_from_slice(b"d\0");
assert!(parse_vds_mappings(&blob, 8).is_err());
// Unknown flag bits are refused.
let blob = [0x01u8, 1, 0, 0, 0, 0, 0, 0, 0, 0x08, b'd', 0];
assert!(parse_vds_mappings(&blob, 8).is_err());
}
#[test]
fn parse_vds_mappings_external_v0() {
// Block version 0 with an explicit (external) source file name.
+401 -260
View File
@@ -191,14 +191,9 @@ fn read_raw_data_full_impl(
offset_size,
length_size,
),
DataLayout::Virtual {
global_heap_address,
global_heap_index,
..
} => read_virtual_data(
DataLayout::Virtual { .. } => read_virtual_data(
file_data,
*global_heap_address,
*global_heap_index,
layout,
dataspace,
datatype,
offset_size,
@@ -465,158 +460,54 @@ pub fn read_raw_data_selection(
}
}
/// Assemble a **Virtual Dataset (VDS)** from its source mappings.
/// Assemble a **Virtual Dataset (VDS)** through the raw-read API, which has no
/// access to the dataset's fill value message.
///
/// Supports virtual datasets of any rank. Same-file sources are read directly;
/// **external-file** sources are read through the caller-supplied `resolver`,
/// which maps a stored source file name to that file's bytes. Each mapping's
/// selected source elements are scattered into the virtual buffer at the
/// positions given by the virtual selection (both enumerated in row-major
/// order, as HDF5 pairs them). Unmapped regions are left at the zero fill value.
///
/// A mapping whose external source file the resolver cannot supply (`None`) is
/// skipped, leaving its region at fill — matching HDF5's tolerance of missing
/// sources. An external source with no resolver at all is a hard error.
/// Delegates to [`crate::vds::read_virtual_dataset`]. Because the fill value
/// is unknown here, a virtual dataset with any element no mapping supplies
/// (an unmapped region, or a missing source file or dataset) is an error
/// rather than a guess at the fill value; so is one whose extent libhdf5
/// would report differently from the stored dataspace (unlimited mappings).
/// Use [`crate::vds::read_virtual_dataset`] to read those.
#[allow(clippy::too_many_arguments)]
fn read_virtual_data(
file_data: &[u8],
global_heap_address: Option<u64>,
global_heap_index: u32,
layout: &DataLayout,
dataspace: &Dataspace,
datatype: &Datatype,
offset_size: u8,
length_size: u8,
resolver: Option<&VdsSourceResolver>,
) -> Result<Vec<u8>, FormatError> {
use crate::data_layout::parse_vds_mappings;
use crate::global_heap::GlobalHeapCollection;
use crate::selection::Selection;
let elem_size = datatype.type_size() as usize;
let mut out = crate::chunked_read::alloc_output(crate::chunked_read::checked_byte_len(
dataspace.checked_num_elements()?,
elem_size,
)?)?;
let virtual_dims = &dataspace.dimensions;
let addr = global_heap_address.ok_or_else(|| {
FormatError::ChunkedReadError("virtual dataset has no mapping global heap".into())
})?;
let coll = GlobalHeapCollection::parse(file_data, addr as usize, length_size)?;
let obj =
coll.get_object(global_heap_index as u16)
.ok_or(FormatError::GlobalHeapObjectNotFound {
collection_address: addr,
index: global_heap_index as u16,
})?;
let mappings = parse_vds_mappings(&obj.data, length_size)?;
for m in &mappings {
let same_file = m.source_file.is_empty() || m.source_file == ".";
// Resolve the bytes of the file holding this source dataset.
let external;
let src_file_data: &[u8] = if same_file {
file_data
} else {
let r = resolver.ok_or_else(|| {
FormatError::ChunkedReadError(
"external-file virtual dataset sources require a file resolver".into(),
)
})?;
match r(&m.source_file) {
Some(bytes) => {
external = bytes;
&external
}
// Source file unavailable: leave this region at fill value.
None => continue,
}
};
let (vsel, _) = Selection::decode_serialized(&m.virtual_selection)?;
let (ssel, _) = Selection::decode_serialized(&m.source_selection)?;
let (src_raw, src_dims) =
read_named_dataset_raw(src_file_data, &m.source_dataset, offset_size, length_size)?;
let vidx = vsel.iter_linear(virtual_dims)?;
let sidx = ssel.iter_linear(&src_dims)?;
if vidx.len() != sidx.len() {
return Err(FormatError::ChunkedReadError(
"virtual/source selection element counts differ".into(),
));
}
for (&v, &s) in vidx.iter().zip(sidx.iter()) {
let (vo, so) = (v as usize * elem_size, s as usize * elem_size);
if vo + elem_size > out.len() || so + elem_size > src_raw.len() {
return Err(FormatError::ChunkedReadError(
"virtual dataset selection out of bounds".into(),
));
}
out[vo..vo + elem_size].copy_from_slice(&src_raw[so..so + elem_size]);
}
}
Ok(out)
}
/// Read a named dataset's raw (decoded) bytes and its dimensions, navigating
/// from the superblock. Used to pull VDS source datasets out of the same file.
fn read_named_dataset_raw(
file_data: &[u8],
path: &str,
_offset_size: u8,
_length_size: u8,
) -> Result<(Vec<u8>, Vec<u64>), FormatError> {
use crate::filter_pipeline::FilterPipeline;
use crate::group_v2::resolve_path_any;
use crate::message_type::MessageType;
use crate::object_header::ObjectHeader;
use crate::signature::find_signature;
use crate::superblock::Superblock;
let sig = find_signature(file_data)?;
let sb = Superblock::parse(file_data, sig)?;
let addr = resolve_path_any(file_data, &sb, path)?;
let hdr = ObjectHeader::parse(file_data, addr as usize, sb.offset_size, sb.length_size)?;
let find = |t: MessageType| hdr.messages.iter().find(|m| m.msg_type == t);
let ds_msg = find(MessageType::Dataspace)
.ok_or_else(|| FormatError::ChunkedReadError("VDS source has no dataspace".into()))?;
let dataspace = Dataspace::parse(&ds_msg.data, sb.length_size)?;
let dt_msg = find(MessageType::Datatype)
.ok_or_else(|| FormatError::ChunkedReadError("VDS source has no datatype".into()))?;
let (datatype, _) = Datatype::parse(&dt_msg.data)?;
let dl_msg = find(MessageType::DataLayout)
.ok_or_else(|| FormatError::ChunkedReadError("VDS source has no data layout".into()))?;
let layout = DataLayout::parse(&dl_msg.data, sb.offset_size, sb.length_size)?;
// A virtual dataset whose source is itself another virtual dataset could
// form a cycle (A -> B -> A) and recurse into a stack overflow. Nested
// virtual sources are exotic and unsupported, so stop here cleanly.
if matches!(layout, DataLayout::Virtual { .. }) {
return Err(FormatError::ChunkedReadError(
"virtual dataset source is itself virtual (unsupported)".into(),
));
}
let pipeline = find(MessageType::FilterPipeline)
.map(|m| FilterPipeline::parse(&m.data))
.transpose()?;
let raw = read_raw_data_full(
let wrapped =
resolver.map(|r| move |name: &str| -> Result<Option<Vec<u8>>, FormatError> { Ok(r(name)) });
let wrapped_ref = wrapped.as_ref().map(|w| w as &crate::vds::VdsFileResolver);
let v = crate::vds::read_virtual_dataset(
file_data,
&layout,
&dataspace,
&datatype,
pipeline.as_ref(),
sb.offset_size,
sb.length_size,
layout,
dataspace,
datatype,
None,
offset_size,
length_size,
wrapped_ref,
)?;
Ok((raw, dataspace.dimensions.clone()))
if v.dims != dataspace.dimensions {
return Err(FormatError::ChunkedReadError(
"virtual dataset extent differs from its stored dataspace; \
read it with vds::read_virtual_dataset"
.into(),
));
}
if v.unmapped > 0 {
return Err(FormatError::ChunkedReadError(
"virtual dataset has elements no source supplies, which read as its \
fill value; read it with vds::read_virtual_dataset and the fill value"
.into(),
));
}
Ok(v.data)
}
/// Extract selected elements from a full dataset buffer.
pub fn extract_selection_from_buffer(
full_data: &[u8],
@@ -773,14 +664,7 @@ pub fn read_as_f64_zerocopy<'a>(raw: &'a [u8], datatype: &Datatype) -> Option<&'
// Only native LE f64 is eligible
#[cfg(target_endian = "little")]
{
if !matches!(
datatype,
Datatype::FloatingPoint {
size: 8,
byte_order: DatatypeByteOrder::LittleEndian,
..
}
) {
if !is_native_le_float(datatype, FloatFormat::Double) {
return None;
}
if !raw.len().is_multiple_of(8) {
@@ -809,14 +693,7 @@ pub fn read_as_f64_zerocopy<'a>(raw: &'a [u8], datatype: &Datatype) -> Option<&'
pub fn read_as_f32_zerocopy<'a>(raw: &'a [u8], datatype: &Datatype) -> Option<&'a [f32]> {
#[cfg(target_endian = "little")]
{
if !matches!(
datatype,
Datatype::FloatingPoint {
size: 4,
byte_order: DatatypeByteOrder::LittleEndian,
..
}
) {
if !is_native_le_float(datatype, FloatFormat::Single) {
return None;
}
if !raw.len().is_multiple_of(4) {
@@ -902,9 +779,9 @@ fn native_le_to_vec<T: Copy>(raw: &[u8], count: usize) -> Vec<T> {
/// Convert raw bytes to `f64` values.
pub fn read_as_f64(raw: &[u8], datatype: &Datatype) -> Result<Vec<f64>, FormatError> {
// Array datatypes (e.g. an array-typed compound member) are read as a flat
// sequence of their base elements.
if let Datatype::Array { base_type, .. } = datatype {
// Array datatypes read as a flat sequence of their base elements, and
// enumerations (h5py's bool among them) as their integer values.
if let Datatype::Array { base_type, .. } | Datatype::Enumeration { base_type, .. } = datatype {
return read_as_f64(raw, base_type);
}
ensure_numeric(datatype, "FloatingPoint or FixedPoint")?;
@@ -919,20 +796,19 @@ pub fn read_as_f64(raw: &[u8], datatype: &Datatype) -> Result<Vec<f64>, FormatEr
// Fast path: native-endian f64 — single bulk memcpy
#[cfg(target_endian = "little")]
if matches!(
datatype,
Datatype::FloatingPoint {
size: 8,
byte_order: DatatypeByteOrder::LittleEndian,
..
}
) {
if is_native_le_float(datatype, FloatFormat::Double) {
return Ok(native_le_to_vec::<f64>(raw, count));
}
let order = get_byte_order(datatype);
let mut result = Vec::with_capacity(count);
if let Datatype::FloatingPoint { .. } = datatype {
let format = FloatFormat::of(datatype)?;
for chunk in raw.chunks_exact(elem_size) {
result.push(format.decode(chunk, &order));
}
return Ok(result);
}
for i in 0..count {
let chunk = &raw[i * elem_size..(i + 1) * elem_size];
let val = convert_to_f64(chunk, datatype, &order)?;
@@ -947,18 +823,7 @@ fn convert_to_f64(
order: &DatatypeByteOrder,
) -> Result<f64, FormatError> {
match dt {
Datatype::FloatingPoint { size, .. } => match size {
4 => {
let v = read_f32_bytes(bytes, order);
Ok(v as f64)
}
8 => Ok(read_f64_bytes(bytes, order)),
2 => Ok(read_f16_bytes(bytes, order) as f64),
_ => Err(FormatError::DataSizeMismatch {
expected: 8,
actual: *size as usize,
}),
},
Datatype::FloatingPoint { .. } => Ok(FloatFormat::of(dt)?.decode(bytes, order)),
Datatype::FixedPoint {
size,
signed,
@@ -982,9 +847,83 @@ fn convert_to_f64(
}
}
/// One numeric element as stored, before conversion to the caller's type.
#[derive(Debug, Clone, Copy, PartialEq)]
enum Scalar {
Signed(i64),
Unsigned(u64),
Float(f64),
}
impl Scalar {
// Every conversion follows libhdf5's default (hard) conversions: a value
// outside the target type's range saturates to its minimum or maximum —
// including a negative value read as unsigned, which reads as 0 — rather
// than being truncated to its low bits. Floats truncate toward zero; NaN
// converts to 0 (libhdf5 leaves that case to the C cast, whose result is
// platform-dependent).
fn to_i64(self) -> i64 {
match self {
Scalar::Signed(v) => v,
Scalar::Unsigned(v) => i64::try_from(v).unwrap_or(i64::MAX),
Scalar::Float(v) => v as i64,
}
}
fn to_u64(self) -> u64 {
match self {
Scalar::Signed(v) => u64::try_from(v).unwrap_or(0),
Scalar::Unsigned(v) => v,
Scalar::Float(v) => v as u64,
}
}
fn to_i32(self) -> i32 {
match self {
Scalar::Signed(v) => v.clamp(i32::MIN.into(), i32::MAX.into()) as i32,
Scalar::Unsigned(v) => i32::try_from(v).unwrap_or(i32::MAX),
Scalar::Float(v) => v as i32,
}
}
}
/// Decode one element of a numeric datatype.
fn decode_scalar(
bytes: &[u8],
dt: &Datatype,
order: &DatatypeByteOrder,
) -> Result<Scalar, FormatError> {
match dt {
Datatype::FixedPoint {
size,
signed,
bit_offset,
bit_precision,
..
} => {
let full = read_unsigned_int(bytes, *size as usize, order);
let (off, prec) = effective_bits(*size as usize, *bit_offset, *bit_precision);
Ok(if *signed {
Scalar::Signed(extract_signed(full, off, prec))
} else {
Scalar::Unsigned(extract_unsigned(full, off, prec))
})
}
_ => convert_to_f64(bytes, dt, order).map(Scalar::Float),
}
}
/// Convert raw bytes to `i64` values.
///
/// Values are converted the way libhdf5 converts them: integers outside the
/// target range saturate at its minimum or maximum (a negative value read as
/// unsigned is 0), and floating-point data is truncated toward zero and
/// saturated, with NaN read as 0.
pub fn read_as_i64(raw: &[u8], datatype: &Datatype) -> Result<Vec<i64>, FormatError> {
if let Datatype::Array { base_type, .. } = datatype {
// Array datatypes read as a flat sequence of their base elements, and
// enumerations (h5py's bool among them) as their integer values.
if let Datatype::Array { base_type, .. } | Datatype::Enumeration { base_type, .. } = datatype {
return read_as_i64(raw, base_type);
}
ensure_numeric(datatype, "FixedPoint (signed)")?;
@@ -1014,19 +953,24 @@ pub fn read_as_i64(raw: &[u8], datatype: &Datatype) -> Result<Vec<i64>, FormatEr
}
let order = get_byte_order(datatype);
let (off, prec) = fixed_bits(datatype);
let mut result = Vec::with_capacity(count);
for i in 0..count {
let chunk = &raw[i * elem_size..(i + 1) * elem_size];
let full = read_unsigned_int(chunk, elem_size, &order);
result.push(extract_signed(full, off, prec));
result.push(decode_scalar(chunk, datatype, &order)?.to_i64());
}
Ok(result)
}
/// Convert raw bytes to `u64` values.
///
/// Values are converted the way libhdf5 converts them: integers outside the
/// target range saturate at its minimum or maximum (a negative value read as
/// unsigned is 0), and floating-point data is truncated toward zero and
/// saturated, with NaN read as 0.
pub fn read_as_u64(raw: &[u8], datatype: &Datatype) -> Result<Vec<u64>, FormatError> {
if let Datatype::Array { base_type, .. } = datatype {
// Array datatypes read as a flat sequence of their base elements, and
// enumerations (h5py's bool among them) as their integer values.
if let Datatype::Array { base_type, .. } | Datatype::Enumeration { base_type, .. } = datatype {
return read_as_u64(raw, base_type);
}
ensure_numeric(datatype, "FixedPoint (unsigned)")?;
@@ -1039,19 +983,19 @@ pub fn read_as_u64(raw: &[u8], datatype: &Datatype) -> Result<Vec<u64>, FormatEr
}
let count = raw.len() / elem_size;
let order = get_byte_order(datatype);
let (off, prec) = fixed_bits(datatype);
let mut result = Vec::with_capacity(count);
for i in 0..count {
let chunk = &raw[i * elem_size..(i + 1) * elem_size];
let full = read_unsigned_int(chunk, elem_size, &order);
result.push(extract_unsigned(full, off, prec));
result.push(decode_scalar(chunk, datatype, &order)?.to_u64());
}
Ok(result)
}
/// Convert raw bytes to `f32` values.
pub fn read_as_f32(raw: &[u8], datatype: &Datatype) -> Result<Vec<f32>, FormatError> {
if let Datatype::Array { base_type, .. } = datatype {
// Array datatypes read as a flat sequence of their base elements, and
// enumerations (h5py's bool among them) as their integer values.
if let Datatype::Array { base_type, .. } | Datatype::Enumeration { base_type, .. } = datatype {
return read_as_f32(raw, base_type);
}
ensure_numeric(datatype, "FloatingPoint")?;
@@ -1066,31 +1010,36 @@ pub fn read_as_f32(raw: &[u8], datatype: &Datatype) -> Result<Vec<f32>, FormatEr
// Fast path: native-endian f32 — single bulk memcpy
#[cfg(target_endian = "little")]
if matches!(
datatype,
Datatype::FloatingPoint {
size: 4,
byte_order: DatatypeByteOrder::LittleEndian,
..
}
) {
if is_native_le_float(datatype, FloatFormat::Single) {
return Ok(native_le_to_vec::<f32>(raw, count));
}
// Little-endian IEEE half precision (numpy float16): widen directly.
if is_native_le_float(datatype, FloatFormat::Half) {
let (halves, _) = raw[..count * 2].as_chunks::<2>();
return Ok(halves
.iter()
.map(|&b| f16_bits_to_f32(u16::from_le_bytes(b)))
.collect());
}
let order = get_byte_order(datatype);
let mut result = Vec::with_capacity(count);
if let Datatype::FloatingPoint { .. } = datatype {
let format = FloatFormat::of(datatype)?;
for chunk in raw.chunks_exact(elem_size) {
result.push(match format {
FloatFormat::Single => read_f32_bytes(chunk, &order),
FloatFormat::Half => read_f16_bytes(chunk, &order),
// Double rounds; every other supported layout (bfloat16, FP8)
// is exact in f32.
_ => format.decode(chunk, &order) as f32,
});
}
return Ok(result);
}
for i in 0..count {
let chunk = &raw[i * elem_size..(i + 1) * elem_size];
match datatype {
Datatype::FloatingPoint { size: 4, .. } => {
result.push(read_f32_bytes(chunk, &order));
}
Datatype::FloatingPoint { size: 8, .. } => {
result.push(read_f64_bytes(chunk, &order) as f32);
}
Datatype::FloatingPoint { size: 2, .. } => {
result.push(read_f16_bytes(chunk, &order));
}
Datatype::FixedPoint {
signed: true,
size,
@@ -1125,8 +1074,15 @@ pub fn read_as_f32(raw: &[u8], datatype: &Datatype) -> Result<Vec<f32>, FormatEr
}
/// Convert raw bytes to `i32` values.
///
/// Values are converted the way libhdf5 converts them: integers outside the
/// target range saturate at its minimum or maximum (a negative value read as
/// unsigned is 0), and floating-point data is truncated toward zero and
/// saturated, with NaN read as 0.
pub fn read_as_i32(raw: &[u8], datatype: &Datatype) -> Result<Vec<i32>, FormatError> {
if let Datatype::Array { base_type, .. } = datatype {
// Array datatypes read as a flat sequence of their base elements, and
// enumerations (h5py's bool among them) as their integer values.
if let Datatype::Array { base_type, .. } | Datatype::Enumeration { base_type, .. } = datatype {
return read_as_i32(raw, base_type);
}
ensure_numeric(datatype, "FixedPoint")?;
@@ -1147,6 +1103,7 @@ pub fn read_as_i32(raw: &[u8], datatype: &Datatype) -> Result<Vec<i32>, FormatEr
datatype,
Datatype::FixedPoint {
byte_order: DatatypeByteOrder::LittleEndian,
signed: true,
..
}
)
@@ -1155,12 +1112,10 @@ pub fn read_as_i32(raw: &[u8], datatype: &Datatype) -> Result<Vec<i32>, FormatEr
}
let order = get_byte_order(datatype);
let (off, prec) = fixed_bits(datatype);
let mut result = Vec::with_capacity(count);
for i in 0..count {
let chunk = &raw[i * elem_size..(i + 1) * elem_size];
let full = read_unsigned_int(chunk, elem_size, &order);
result.push(extract_signed(full, off, prec) as i32);
result.push(decode_scalar(chunk, datatype, &order)?.to_i32());
}
Ok(result)
}
@@ -1601,6 +1556,174 @@ fn reorder_bytes(bytes: &[u8], order: &DatatypeByteOrder) -> [u8; 8] {
buf
}
/// How the bits of a floating-point datatype are laid out, read from the
/// datatype message's fields rather than assumed from its size (a 2-byte
/// float may be IEEE half or bfloat16).
#[derive(Debug, Clone, Copy, PartialEq)]
enum FloatFormat {
/// IEEE-754 binary16.
Half,
/// IEEE-754 binary32.
Single,
/// IEEE-754 binary64.
Double,
/// Any other IEEE-style layout (implied leading mantissa bit, all-ones
/// exponent for infinity/NaN) whose values are all exact in `f64`:
/// bfloat16, the FP8 formats, and similar.
Other(FloatLayout),
}
#[derive(Debug, Clone, Copy, PartialEq)]
struct FloatLayout {
exponent_location: u32,
exponent_size: u32,
mantissa_location: u32,
mantissa_size: u32,
exponent_bias: u32,
}
impl FloatFormat {
fn of(dt: &Datatype) -> Result<FloatFormat, FormatError> {
let Datatype::FloatingPoint {
size,
exponent_location,
exponent_size,
mantissa_location,
mantissa_size,
exponent_bias,
..
} = dt
else {
return Err(FormatError::TypeMismatch {
expected: "FloatingPoint",
actual: datatype_name(dt),
});
};
let layout = FloatLayout {
exponent_location: u32::from(*exponent_location),
exponent_size: u32::from(*exponent_size),
mantissa_location: u32::from(*mantissa_location),
mantissa_size: u32::from(*mantissa_size),
exponent_bias: *exponent_bias,
};
let fields = (
layout.exponent_location,
layout.exponent_size,
layout.mantissa_location,
layout.mantissa_size,
layout.exponent_bias,
);
let bits = size.saturating_mul(8);
// The sign bit is not kept in `Datatype`; every standard layout has it
// directly above the exponent, with the mantissa below.
let well_formed = layout.exponent_size > 0
&& layout.mantissa_size > 0
&& layout.mantissa_location + layout.mantissa_size <= layout.exponent_location
&& layout.exponent_location + layout.exponent_size < bits;
match (size, fields) {
(2, (10, 5, 0, 10, 15)) => Ok(FloatFormat::Half),
(4, (23, 8, 0, 23, 127)) => Ok(FloatFormat::Single),
(8, (52, 11, 0, 52, 1023)) => Ok(FloatFormat::Double),
_ if well_formed
&& *size <= 8
&& layout.exponent_size <= 11
&& layout.mantissa_size <= 52 =>
{
Ok(FloatFormat::Other(layout))
}
// Fields that cannot describe any float (e.g. left zeroed by a
// hand-built datatype): fall back to the IEEE type of that size.
(2, _) if !well_formed => Ok(FloatFormat::Half),
(4, _) if !well_formed => Ok(FloatFormat::Single),
(8, _) if !well_formed => Ok(FloatFormat::Double),
// x87 80-bit extended, binary128, ...: not representable in f64.
_ => Err(FormatError::TypeMismatch {
expected: "floating point of at most 64 bits (IEEE-style layout)",
actual: "FloatingPoint",
}),
}
}
fn decode(self, bytes: &[u8], order: &DatatypeByteOrder) -> f64 {
match self {
FloatFormat::Half => f64::from(read_f16_bytes(bytes, order)),
FloatFormat::Single => f64::from(read_f32_bytes(bytes, order)),
FloatFormat::Double => read_f64_bytes(bytes, order),
FloatFormat::Other(layout) => {
layout.decode(read_unsigned_int(bytes, bytes.len(), order))
}
}
}
}
impl FloatLayout {
/// Decode the value held in the low `size * 8` bits of `bits`.
fn decode(self, bits: u64) -> f64 {
let field = |location: u32, size: u32| (bits >> location) & ((1u64 << size) - 1);
let exponent = field(self.exponent_location, self.exponent_size);
let mantissa = field(self.mantissa_location, self.mantissa_size);
let negative = field(self.exponent_location + self.exponent_size, 1) == 1;
let max_exponent = (1u64 << self.exponent_size) - 1;
let magnitude = if exponent == max_exponent {
if mantissa == 0 {
f64::INFINITY
} else {
f64::NAN
}
} else {
let bias = i64::from(self.exponent_bias);
let msize = i64::from(self.mantissa_size);
// value = significand * 2^power, with an implied leading 1 unless
// the number is subnormal (exponent field 0).
let (significand, power) = if exponent == 0 {
(mantissa, 1 - bias - msize)
} else {
(
mantissa | (1u64 << self.mantissa_size),
exponent as i64 - bias - msize,
)
};
scale_by_pow2(significand as f64, power)
};
if negative { -magnitude } else { magnitude }
}
}
/// `x * 2^power` without `std` (no `powi`/`libm`). `x` is a non-negative
/// integer below 2^53, so it is exact.
fn scale_by_pow2(x: f64, power: i64) -> f64 {
if x == 0.0 || power < -1200 {
return 0.0;
}
if power > 1100 {
return f64::INFINITY;
}
let pow2 = |p: i64| f64::from_bits(((p + 1023) as u64) << 52);
let mut x = x;
let mut power = power;
while power > 1023 {
x *= pow2(1023);
power -= 1023;
}
while power < -1022 {
x *= pow2(-1022);
power += 1022;
}
x * pow2(power)
}
/// Whether `datatype` is the little-endian IEEE float `format`, whose bytes
/// can be copied straight into native values on a little-endian target.
fn is_native_le_float(datatype: &Datatype, format: FloatFormat) -> bool {
matches!(
datatype,
Datatype::FloatingPoint {
byte_order: DatatypeByteOrder::LittleEndian,
..
}
) && FloatFormat::of(datatype).is_ok_and(|f| f == format)
}
fn read_f64_bytes(bytes: &[u8], order: &DatatypeByteOrder) -> f64 {
let buf = reorder_bytes(bytes, order);
f64::from_le_bytes(buf)
@@ -1622,36 +1745,7 @@ fn read_f16_bytes(bytes: &[u8], order: &DatatypeByteOrder) -> f32 {
f16_bits_to_f32(u16::from_le_bytes(buf))
}
/// Convert the bit pattern of an IEEE-754 half (binary16) to an `f32`.
fn f16_bits_to_f32(h: u16) -> f32 {
let h = h as u32;
let sign = (h & 0x8000) << 16;
let exp = (h >> 10) & 0x1f;
let mant = h & 0x3ff;
let bits = if exp == 0 {
if mant == 0 {
sign // signed zero
} else {
// Subnormal: normalize into an f32 normal.
let mut e: i32 = -1;
let mut m = mant;
loop {
e += 1;
m <<= 1;
if m & 0x400 != 0 {
break;
}
}
let m = m & 0x3ff;
sign | (((127 - 15 - e) as u32) << 23) | (m << 13)
}
} else if exp == 0x1f {
sign | 0x7f80_0000 | (mant << 13) // inf / NaN
} else {
sign | ((exp + (127 - 15)) << 23) | (mant << 13)
};
f32::from_bits(bits)
}
use crate::float16::f16_bits_to_f32;
fn read_f32_bytes(bytes: &[u8], order: &DatatypeByteOrder) -> f32 {
let mut buf = [0u8; 4];
@@ -1680,20 +1774,6 @@ fn effective_bits(size: usize, bit_offset: u16, bit_precision: u16) -> (u32, u32
(bit_offset as u32, prec)
}
/// `(bit_offset, bit_precision)` for a fixed-point datatype, full width for
/// other types.
fn fixed_bits(datatype: &Datatype) -> (u32, u32) {
match datatype {
Datatype::FixedPoint {
size,
bit_offset,
bit_precision,
..
} => effective_bits(*size as usize, *bit_offset, *bit_precision),
_ => (0, 0),
}
}
/// Whether a datatype occupies its full storage width (bit offset 0, precision
/// == size·8), in which case the bulk-copy fast read paths apply. Non
/// fixed-point types are treated as full width.
@@ -1906,6 +1986,67 @@ mod tests {
assert_eq!(read_as_u64(&raw, &dt).unwrap(), vec![4095, 1, 2048]);
}
#[test]
fn float_to_int_truncates_and_saturates() {
// Values libhdf5 hands to an undefined C cast: NaN reads as 0 and
// exactly 2^63 saturates instead of wrapping to i64::MIN.
let dt = make_f64_le_type();
let vals = [f64::NAN, 2f64.powi(63), -2.5, 2.0f64.powi(64)];
let raw: Vec<u8> = vals.iter().flat_map(|v| v.to_le_bytes()).collect();
assert_eq!(
read_as_i64(&raw, &dt).unwrap(),
vec![0, i64::MAX, -2, i64::MAX]
);
assert_eq!(
read_as_u64(&raw, &dt).unwrap(),
vec![0, 1 << 63, 0, u64::MAX]
);
assert_eq!(
read_as_i32(&raw, &dt).unwrap(),
vec![0, i32::MAX, -2, i32::MAX]
);
}
#[test]
fn bfloat16_and_fp8_decode_by_fields() {
// bfloat16 is a 2-byte float that is not IEEE half.
let bf16 = Datatype::FloatingPoint {
size: 2,
byte_order: DatatypeByteOrder::LittleEndian,
bit_offset: 0,
bit_precision: 16,
exponent_location: 7,
exponent_size: 8,
mantissa_location: 0,
mantissa_size: 7,
exponent_bias: 127,
};
let raw: Vec<u8> = [0x3FC0u16, 0xC010, 0x7F80, 0x0001]
.iter()
.flat_map(|v| v.to_le_bytes())
.collect();
let got = read_as_f64(&raw, &bf16).unwrap();
assert_eq!(&got[..3], &[1.5, -2.25, f64::INFINITY]);
assert_eq!(got[3], 2f64.powi(-133)); // smallest subnormal
assert_eq!(read_as_f32(&raw, &bf16).unwrap()[..2], [1.5, -2.25]);
// FP8 E4M3: 1, -1, 2, 0, NaN (IEEE-style, as libhdf5 treats it).
let e4m3 = Datatype::FloatingPoint {
size: 1,
byte_order: DatatypeByteOrder::LittleEndian,
bit_offset: 0,
bit_precision: 8,
exponent_location: 3,
exponent_size: 4,
mantissa_location: 0,
mantissa_size: 3,
exponent_bias: 7,
};
let got = read_as_f64(&[0x38, 0xB8, 0x40, 0x00, 0x7E], &e4m3).unwrap();
assert_eq!(&got[..4], &[1.0, -1.0, 2.0, 0.0]);
assert!(got[4].is_nan());
}
#[test]
fn full_width_signed_unchanged() {
// Regression: full-width 32-bit signed must be unaffected.
+321 -8
View File
@@ -4,7 +4,7 @@
//! for compound, enumeration, variable-length, and array types.
#[cfg(not(feature = "std"))]
use alloc::{boxed::Box, string::String, vec, vec::Vec};
use alloc::{boxed::Box, format, string::String, vec, vec::Vec};
use byteorder::{ByteOrder, LittleEndian};
@@ -137,6 +137,17 @@ pub enum Datatype {
},
}
/// Longest opaque tag that can be stored: its NUL-padded length must fit
/// the 8-bit length in the datatype's class bits.
pub const MAX_OPAQUE_TAG_LEN: usize = 248;
/// An opaque tag up to (not including) its first NUL.
fn opaque_tag_text(tag: &[u8]) -> &[u8] {
tag.iter()
.position(|&b| b == 0)
.map_or(tag, |end| &tag[..end])
}
fn ensure_len(data: &[u8], offset: usize, needed: usize) -> Result<(), FormatError> {
match offset.checked_add(needed) {
Some(end) if end <= data.len() => Ok(()),
@@ -361,7 +372,10 @@ impl Datatype {
// Opaque
let tag_len = bf0 as usize;
ensure_len(data, pos, tag_len)?;
let tag = data[pos..pos + tag_len].to_vec();
// The stored tag is NUL-padded to a multiple of 8 bytes; the
// tag itself ends at the first NUL (libhdf5 reads it with
// `strndup`).
let tag = opaque_tag_text(&data[pos..pos + tag_len]).to_vec();
// Tags are padded to multiple of 8 bytes
let padded = (tag_len + 7) & !7;
let pos = 8 + padded; // from start of properties
@@ -409,13 +423,44 @@ impl Datatype {
ensure_len(data, pos, 4)?;
let byte_offset = LittleEndian::read_u32(&data[pos..pos + 4]) as u64;
pos += 4;
// v1 members can be fixed-size arrays of the member
// type (libhdf5 builds an array type from these
// fields; the permutation is ignored, as libhdf5
// does). Skipping them read a `[4] i32` member as
// one `i32`.
let mut array_dims = Vec::new();
if version == 1 {
ensure_len(data, pos, 28)?;
let ndims = data[pos] as usize;
// libhdf5 refuses more than four dimensions and,
// when building the array type, a zero-sized one.
let zero_dim = (0..ndims.min(4)).any(|j| {
let at = pos + 12 + 4 * j;
LittleEndian::read_u32(&data[at..at + 4]) == 0
});
if ndims > 4 || zero_dim {
return Err(FormatError::InvalidDatatypeVersion {
class: class_id,
version,
});
}
array_dims = (0..ndims)
.map(|j| {
let at = pos + 12 + 4 * j;
LittleEndian::read_u32(&data[at..at + 4])
})
.collect();
pos += 28;
}
let (member_dt, consumed) =
let (mut member_dt, consumed) =
Self::parse_with_depth(&data[pos..], depth + 1)?;
pos += consumed;
if !array_dims.is_empty() {
member_dt = Datatype::Array {
base_type: Box::new(member_dt),
dimensions: array_dims,
};
}
members.push(CompoundMember {
name,
byte_offset,
@@ -640,7 +685,8 @@ impl Datatype {
mantissa_size,
exponent_bias,
} => {
let mut bf0 = 0x20u8; // bit 5: sign location bit (standard IEEE 754)
// Bits 4-5: mantissa normalization = 2 (implied leading 1, IEEE 754).
let mut bf0 = 0x20u8;
match byte_order {
DatatypeByteOrder::BigEndian => {
bf0 |= 0x01;
@@ -650,9 +696,14 @@ impl Datatype {
}
_ => {}
}
// bf[1] bits 0-1: mantissa normalization = 2 (MSB not stored, IEEE 754)
let bf1 = 0x3fu8; // matching what h5py generates
let mut buf = Self::build_header(1, 1, [bf0, bf1, 0], *size);
// Bits 8-15: the sign bit's position, the top bit of the value.
// This was hard-coded to 63, which is right only for f64: the
// HDF5 library rejects any other float with "sign bit position
// out of bounds", so every f32 dataset and attribute we wrote
// was unreadable by h5py and libhdf5.
let sign_location =
(u32::from(*bit_offset) + u32::from(*bit_precision)).saturating_sub(1) as u8;
let mut buf = Self::build_header(1, 1, [bf0, sign_location, 0], *size);
buf.extend_from_slice(&bit_offset.to_le_bytes());
buf.extend_from_slice(&bit_precision.to_le_bytes());
buf.push(*exponent_location);
@@ -761,7 +812,77 @@ impl Datatype {
buf.extend_from_slice(&base_type.serialize());
buf
}
_ => Vec::new(),
Datatype::Time {
size,
bit_precision,
} => {
// Byte order is not modelled for time types; write little-endian.
let mut buf = Self::build_header(2, 1, [0, 0, 0], *size);
buf.extend_from_slice(&bit_precision.to_le_bytes());
buf
}
Datatype::BitField {
size,
byte_order,
bit_offset,
bit_precision,
} => {
let bf0 = u8::from(matches!(byte_order, DatatypeByteOrder::BigEndian));
let mut buf = Self::build_header(4, 1, [bf0, 0, 0], *size);
buf.extend_from_slice(&bit_offset.to_le_bytes());
buf.extend_from_slice(&bit_precision.to_le_bytes());
buf
}
Datatype::Opaque { size, tag } => {
// The tag is stored NUL-padded to a multiple of 8 bytes and the
// padded length goes in the class bits, as libhdf5 writes it.
// A tag longer than MAX_OPAQUE_TAG_LEN cannot be encoded;
// `check_encodable` rejects it before a file is written.
let tag = opaque_tag_text(tag);
let tag = &tag[..tag.len().min(MAX_OPAQUE_TAG_LEN)];
let padded = tag.len().div_ceil(8) * 8;
let mut buf = Self::build_header(5, 1, [padded as u8, 0, 0], *size);
buf.extend_from_slice(tag);
buf.resize(8 + padded, 0);
buf
}
Datatype::Reference { size, ref_type } => {
// Legacy references are datatype version 1; the H5T_STD_REF
// kinds only exist from version 4, which also carries their
// encoding version (1) in the high nibble.
let (version, bf0) = match ref_type {
ReferenceType::Object => (1, 0),
ReferenceType::DatasetRegion => (1, 1),
ReferenceType::Object2 => (4, 0x12),
ReferenceType::DatasetRegion2 => (4, 0x13),
ReferenceType::Attribute => (4, 0x14),
};
Self::build_header(7, version, [bf0, 0, 0], *size)
}
}
}
/// Check that this datatype can be written: every part of it has an
/// on-disk encoding. [`Self::serialize`] cannot report errors, so the
/// writer calls this first.
pub fn check_encodable(&self) -> Result<(), FormatError> {
match self {
Datatype::Opaque { tag, .. } if opaque_tag_text(tag).len() > MAX_OPAQUE_TAG_LEN => {
Err(FormatError::SerializationError(format!(
"opaque tag is {} bytes; at most {MAX_OPAQUE_TAG_LEN} can be stored",
opaque_tag_text(tag).len()
)))
}
Datatype::String { size: 0, .. } => Err(FormatError::SerializationError(
"fixed-length string datatype of size 0 (libhdf5 requires at least 1 byte)".into(),
)),
Datatype::Compound { members, .. } => members
.iter()
.try_for_each(|m| m.datatype.check_encodable()),
Datatype::Enumeration { base_type, .. }
| Datatype::VariableLength { base_type, .. }
| Datatype::Array { base_type, .. } => base_type.check_encodable(),
_ => Ok(()),
}
}
@@ -818,6 +939,24 @@ fn build_dt_header(class: u8, version: u8, bf: [u8; 3], size: u32) -> Vec<u8> {
mod tests {
use super::*;
#[test]
fn float_sign_location_is_the_top_bit_of_the_value() {
// The HDF5 library rejects a float whose sign position is not inside
// its precision; this was hard-coded to 63, so every f32 we wrote was
// unreadable by h5py. Byte 2 of the message is the sign position.
use crate::type_builders::{make_f16_type, make_f32_type, make_f64_type};
for (dt, sign) in [
(make_f16_type(), 15),
(make_f32_type(), 31),
(make_f64_type(), 63),
] {
let bytes = dt.serialize();
assert_eq!(bytes[2], sign, "{dt:?}");
let (parsed, _) = Datatype::parse(&bytes).unwrap();
assert_eq!(parsed, dt);
}
}
// Helper to build a fixed-point datatype message
fn build_fixed_point(
size: u32,
@@ -1228,6 +1367,64 @@ mod tests {
assert_xyid_compound(dt);
}
#[test]
fn test_compound_v1_member_array_fields() {
// HDF5 1.6 wrote array members of a v1 compound through the legacy
// per-member fields (as in libhdf5's tools/test/testfiles/
// tcompound.h5 `type2`: `int_array` [4] i32, `float_array` [5][6]
// f32). They used to be skipped, reading each member as a scalar.
let i32le: [u8; 12] = [
0x10, 0x08, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x00,
];
let mut b = vec![0x16, 0x02, 0x00, 0x00, 0x88, 0x00, 0x00, 0x00];
for (name, offset, dims) in [
(&b"int_array"[..], 0u32, &[4u32][..]),
(&b"xy"[..], 16, &[5u32, 6][..]),
] {
let mut padded = name.to_vec();
padded.resize((name.len() + 1 + 7) & !7, 0);
b.extend_from_slice(&padded);
b.extend_from_slice(&offset.to_le_bytes());
b.push(dims.len() as u8);
b.extend_from_slice(&[0u8; 3 + 4 + 4]); // reserved, permutation, reserved
for j in 0..4 {
b.extend_from_slice(&dims.get(j).copied().unwrap_or(0).to_le_bytes());
}
b.extend_from_slice(&i32le);
}
let (dt, consumed) = Datatype::parse(&b).unwrap();
assert_eq!(consumed, b.len());
let Datatype::Compound { members, .. } = dt else {
panic!("expected Compound, got {dt:?}");
};
let got: Vec<(&str, u64, u32, Option<Vec<u32>>)> = members
.iter()
.map(|m| {
let dims = match &m.datatype {
Datatype::Array { dimensions, .. } => Some(dimensions.clone()),
_ => None,
};
(m.name.as_str(), m.byte_offset, m.datatype.type_size(), dims)
})
.collect();
assert_eq!(
got,
vec![
("int_array", 0, 16, Some(vec![4])),
("xy", 16, 120, Some(vec![5, 6])),
]
);
// More than four dimensions cannot be encoded, and libhdf5 refuses a
// zero-sized dimension (a fuzzed tcompound.h5, cve-2024-32616.h5).
let mut bad = b.clone();
bad[8 + 16 + 4] = 5;
assert!(Datatype::parse(&bad).is_err());
let mut bad = b.clone();
bad[8 + 16 + 4] = 2; // [4, 0]
assert!(Datatype::parse(&bad).is_err());
}
#[test]
fn test_compound_v1_truncated_is_error_not_panic() {
let bytes = compound_v1_bytes();
@@ -1601,6 +1798,122 @@ mod tests {
);
}
fn hex(s: &str) -> Vec<u8> {
(0..s.len())
.step_by(2)
.map(|i| u8::from_str_radix(&s[i..i + 2], 16).unwrap())
.collect()
}
/// `serialize` used to return an empty message for these four classes,
/// which libhdf5 rejects ("ran off end of input buffer while decoding").
/// Expected bytes are libhdf5's own encoding (HDF5 2.0 `H5Tencode`, or the
/// datatype message of an HDF5 2.0 file for `H5T_STD_REF`).
#[test]
fn serialize_matches_libhdf5_for_time_bitfield_opaque_reference() {
let cases = [
(
Datatype::Reference {
size: 8,
ref_type: ReferenceType::Object,
},
"1700000008000000",
),
(
Datatype::Reference {
size: 12,
ref_type: ReferenceType::DatasetRegion,
},
"170100000c000000",
),
(
Datatype::Reference {
size: 18,
ref_type: ReferenceType::Object2,
},
"4712000012000000",
),
(
Datatype::BitField {
size: 1,
byte_order: DatatypeByteOrder::LittleEndian,
bit_offset: 0,
bit_precision: 8,
},
"140000000100000000000800",
),
(
Datatype::BitField {
size: 2,
byte_order: DatatypeByteOrder::BigEndian,
bit_offset: 0,
bit_precision: 16,
},
"140100000200000000001000",
),
(
Datatype::Opaque {
size: 4,
tag: b"mytag".to_vec(),
},
"15080000040000006d79746167000000",
),
(
Datatype::Opaque {
size: 4,
tag: b"12345678".to_vec(),
},
"15080000040000003132333435363738",
),
(
Datatype::Opaque {
size: 4,
tag: vec![],
},
"1500000004000000",
),
(
Datatype::Time {
size: 4,
bit_precision: 32,
},
"12000000040000002000",
),
];
for (dt, expected) in cases {
let bytes = dt.serialize();
assert_eq!(bytes, hex(expected), "{dt:?}");
let (parsed, consumed) = Datatype::parse(&bytes).unwrap();
assert_eq!(parsed, dt);
assert_eq!(consumed, bytes.len());
}
}
#[test]
fn opaque_tag_padding_is_not_part_of_the_tag() {
// libhdf5 pads "mytag" to 8 bytes; parsing must not return the NULs,
// or copying the type would grow the tag.
let (dt, _) = Datatype::parse(&hex("15080000040000006d79746167000000")).unwrap();
assert_eq!(
dt,
Datatype::Opaque {
size: 4,
tag: b"mytag".to_vec()
}
);
let long = Datatype::Opaque {
size: 1,
tag: vec![b'x'; MAX_OPAQUE_TAG_LEN + 1],
};
assert!(long.check_encodable().is_err());
let ok = Datatype::Opaque {
size: 1,
tag: vec![b'x'; MAX_OPAQUE_TAG_LEN],
};
assert!(ok.check_encodable().is_ok());
assert_eq!(Datatype::parse(&ok.serialize()).unwrap().0, ok);
}
#[test]
fn test_error_invalid_reference_type() {
let buf = build_dt_header(7, 1, [5, 0, 0], 8);
+245 -268
View File
@@ -7,7 +7,7 @@ extern crate alloc;
use alloc::{vec, vec::Vec};
use crate::checksum::jenkins_lookup3;
use crate::chunked_write::WrittenChunk;
use crate::chunked_write::{WrittenChunk, filtered_chunk_size_len, push_addr, push_index_element};
/// Serialize a v4 Extensible Array layout message.
pub(crate) fn serialize_v4_extensible_array(
@@ -58,11 +58,11 @@ pub(crate) fn serialize_v4_extensible_array(
buf.push(4);
// EA creation parameters (must match AEHD and HDF5 C library defaults)
buf.push(32); // max_nelmts_bits
buf.push(4); // idx_blk_elmts
buf.push(4); // super_blk_min_data_ptrs
buf.push(16); // data_blk_min_elmts
buf.push(10); // max_dblk_page_nelmts_bits
buf.push(MAX_NELMTS_BITS);
buf.push(IDX_BLK_ELMTS);
buf.push(SUP_BLK_MIN_DATA_PTRS);
buf.push(DATA_BLK_MIN_ELMTS);
buf.push(MAX_DBLK_PAGE_NELMTS_BITS);
// EA header address
match offset_size {
@@ -74,304 +74,281 @@ pub(crate) fn serialize_v4_extensible_array(
buf
}
// EA creation parameters — the HDF5 library's defaults for chunk indexes
// (`H5D_EARRAY_*`); the layout message above and the header must agree.
const MAX_NELMTS_BITS: u8 = 32;
const IDX_BLK_ELMTS: u8 = 4;
const SUP_BLK_MIN_DATA_PTRS: u8 = 4;
const DATA_BLK_MIN_ELMTS: u8 = 16;
const MAX_DBLK_PAGE_NELMTS_BITS: u8 = 10;
/// One data block of the array: its first element (relative to the end of
/// the index block's own elements), element count, and address when it is
/// allocated.
struct DataBlock {
start: usize,
nelmts: usize,
addr: Option<u64>,
}
/// Build a complete Extensible Array at a known absolute address.
///
/// For simplicity, we put all elements inline in the index block when the
/// number of chunks is small (up to idx_blk_elmts), otherwise use inline +
/// direct data blocks.
/// `slots[i]` is the element at linear index `i` (see `chunk_grid`); `None`
/// marks an unallocated chunk. The first `IDX_BLK_ELMTS` elements live in
/// the index block, the rest in data blocks grouped by super block level
/// exactly as `H5EA__hdr_init` sizes them: level `u` has `2^(u/2)` data
/// blocks of `DATA_BLK_MIN_ELMTS * 2^ceil(u/2)` elements. The data blocks of
/// the first levels are addressed straight from the index block; later
/// levels go through a super block (EASB). Data blocks larger than a page
/// (`2^MAX_DBLK_PAGE_NELMTS_BITS` elements) are paged, with their page-init
/// bits kept in the owning super block. Only blocks holding a defined element
/// are allocated; the rest keep the undefined address, as in a file the
/// library wrote.
pub fn build_extensible_array_at(
chunks: &[WrittenChunk],
slots: &[Option<WrittenChunk>],
offset_size: u8,
length_size: u8,
has_filters: bool,
ea_base_address: u64,
) -> Vec<u8> {
let os = offset_size as usize;
let num_elements = chunks.len();
// Compute element encoding size (same logic as Fixed Array)
let chunk_size_bytes: usize = if has_filters {
let max_raw = chunks.iter().map(|c| c.raw_size).max().unwrap_or(1);
let log2_val = if max_raw <= 1 {
0
} else {
63 - max_raw.leading_zeros()
};
let len = 1 + ((log2_val + 8) / 8) as usize;
len.min(8)
} else {
0
};
let elem_size = if has_filters {
os + chunk_size_bytes + 4
} else {
os
};
let chunk_size_bytes = has_filters.then(|| filtered_chunk_size_len(slots));
let elem_size = os + chunk_size_bytes.map_or(0, |n| n + 4);
let client_id: u8 = if has_filters { 1 } else { 0 };
let arr_off_size = (MAX_NELMTS_BITS as usize).div_ceil(8);
let page_nelmts = 1usize << MAX_DBLK_PAGE_NELMTS_BITS;
let idx_blk = IDX_BLK_ELMTS as usize;
// EA creation parameters — must match HDF5 C library defaults exactly
let max_nelmts_bits: u8 = 32;
let idx_blk_elmts: u8 = 4;
let min_dblk_nelmts: u8 = 16;
let super_blk_min_nelmts: u8 = 4;
let max_dblk_nelmts_bits: u8 = 10;
// Elements past the last defined one are never realised
// (`max_idx_set` is one past the highest index ever set).
let max_idx_set = slots.iter().rposition(Option::is_some).map_or(0, |i| i + 1);
let slots = &slots[..max_idx_set];
let defined_in = |start: usize, n: usize| -> bool {
let lo = idx_blk.saturating_add(start).min(slots.len());
let hi = idx_blk
.saturating_add(start)
.saturating_add(n)
.min(slots.len());
slots[lo..hi].iter().any(Option::is_some)
};
// EAHD size: fixed(12) + 6 stats(6*length_size) + addr(offset_size) + checksum(4)
// Super block levels: (ndblks, dblk_nelmts, first element).
let log2_dmin = (DATA_BLK_MIN_ELMTS as u32).trailing_zeros() as usize;
let nsblks = 1 + MAX_NELMTS_BITS as usize - log2_dmin;
let ndblk_addrs = 2 * (SUP_BLK_MIN_DATA_PTRS as usize - 1);
let mut levels: Vec<(usize, usize, usize)> = Vec::with_capacity(nsblks);
let mut start = 0usize;
for u in 0..nsblks {
let ndblks = 1usize << (u / 2);
let nelmts = (DATA_BLK_MIN_ELMTS as usize) << u.div_ceil(2);
levels.push((ndblks, nelmts, start));
// Saturate: on 32-bit targets the last levels only need to compare
// as "beyond the end".
start = start.saturating_add(ndblks.saturating_mul(nelmts));
}
// Levels whose data blocks the index block addresses directly.
let mut direct_levels = 0;
let mut n = 0;
while n < ndblk_addrs {
n += levels[direct_levels].0;
direct_levels += 1;
}
let nsblk_addrs = nsblks - direct_levels;
let dblk_size = |nelmts: usize| -> usize {
let prefix = 4 + 1 + 1 + os + arr_off_size + 4;
if nelmts > page_nelmts {
prefix + (nelmts / page_nelmts) * (page_nelmts * elem_size + 4)
} else {
prefix + nelmts * elem_size
}
};
let sblk_bitmap_len = |ndblks: usize, nelmts: usize| -> usize {
if nelmts > page_nelmts {
ndblks * (nelmts / page_nelmts).div_ceil(8)
} else {
0
}
};
// Plan addresses: header, index block, the direct data blocks, then each
// allocated super block followed by its allocated data blocks.
let aehd_size = 4 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 6 * length_size as usize + os + 4;
let aeib_address = ea_base_address + aehd_size as u64;
let aeib_size = 4 + 1 + 1 + os + idx_blk * elem_size + ndblk_addrs * os + nsblk_addrs * os + 4;
let mut cursor = aeib_address + aeib_size as u64;
// Determine how many elements go inline vs data blocks
let n_inline = (idx_blk_elmts as usize).min(num_elements);
let remaining_after_inline = num_elements.saturating_sub(n_inline);
let mut ndata_blks = 0u64;
let mut data_blk_size = 0u64;
let mut nsuper_blks = 0u64;
let mut super_blk_size = 0u64;
let mut realized = idx_blk as u64;
// Compute super block layout per HDF5 spec
let sblk_min = super_blk_min_nelmts as usize;
let log2_dblk_min = if min_dblk_nelmts <= 1 {
0
} else {
(min_dblk_nelmts as u32).trailing_zeros() as usize
};
let nsblks = (max_nelmts_bits as usize).saturating_sub(log2_dblk_min) + 1;
// Direct data block addresses (from super blocks 0..sblk_min-1)
let mut dblk_sizes: Vec<usize> = Vec::new();
for sblk_idx in 0..sblk_min.min(nsblks) {
let ndblks = 1usize << (sblk_idx / 2);
let dblk_nelmts = (min_dblk_nelmts as usize) * (1 << sblk_idx.div_ceil(2));
for _ in 0..ndblks {
dblk_sizes.push(dblk_nelmts);
let mut plan_dblk = |cursor: &mut u64, start: usize, nelmts: usize| -> DataBlock {
let addr = defined_in(start, nelmts).then(|| {
let a = *cursor;
let size = dblk_size(nelmts) as u64;
*cursor += size;
ndata_blks += 1;
data_blk_size += size;
realized += nelmts as u64;
a
});
DataBlock {
start,
nelmts,
addr,
}
}
let n_direct_dblks = dblk_sizes.len();
// Super block addresses (for super blocks sblk_min..nsblks-1)
let n_sblk_addrs = nsblks.saturating_sub(sblk_min);
// EAIB size
let aeib_size = 4
+ 1
+ 1
+ os
+ idx_blk_elmts as usize * elem_size
+ n_direct_dblks * os
+ n_sblk_addrs * os
+ 4;
// Build AEHD
let mut aehd = Vec::with_capacity(aehd_size);
aehd.extend_from_slice(b"EAHD");
aehd.push(0); // version
aehd.push(client_id);
aehd.push(elem_size as u8);
aehd.push(max_nelmts_bits);
aehd.push(idx_blk_elmts);
aehd.push(min_dblk_nelmts);
aehd.push(super_blk_min_nelmts);
aehd.push(max_dblk_nelmts_bits);
// Count data blocks that will have chunks
let n_active_dblks: u64 = if remaining_after_inline > 0 {
let mut count = 0u64;
let mut ci = n_inline;
for &sz in &dblk_sizes {
if ci < num_elements {
count += 1;
ci += sz;
}
}
count
} else {
0
};
let blk_off_size = (max_nelmts_bits as usize).div_ceil(8);
let aedb_header_overhead = 4 + 1 + 1 + os + blk_off_size + 4;
let data_blk_total_size: u64 = if remaining_after_inline > 0 {
let mut total = 0u64;
let mut ci = n_inline;
for &sz in &dblk_sizes {
if ci < num_elements {
total += (aedb_header_overhead + sz * elem_size) as u64;
ci += sz;
}
}
total
} else {
0
};
let max_idx_set: u64 = if remaining_after_inline > 0 {
let mut max_set = idx_blk_elmts as u64;
let mut ci = n_inline;
for &sz in &dblk_sizes {
if ci < num_elements {
max_set += sz as u64;
ci += sz;
}
}
max_set
} else {
idx_blk_elmts as u64
};
let mut direct: Vec<DataBlock> = Vec::with_capacity(ndblk_addrs);
for &(ndblks, nelmts, first) in &levels[..direct_levels] {
for k in 0..ndblks {
direct.push(plan_dblk(&mut cursor, first + k * nelmts, nelmts));
}
}
// (super block address, level, its data blocks)
let mut supers: Vec<(Option<u64>, usize, Vec<DataBlock>)> = Vec::with_capacity(nsblk_addrs);
for (u, &(ndblks, nelmts, first)) in levels.iter().enumerate().skip(direct_levels) {
if !defined_in(first, ndblks.saturating_mul(nelmts)) {
supers.push((None, u, Vec::new()));
continue;
}
let sb_size =
4 + 1 + 1 + os + arr_off_size + sblk_bitmap_len(ndblks, nelmts) + ndblks * os + 4;
let sb_addr = cursor;
cursor += sb_size as u64;
nsuper_blks += 1;
super_blk_size += sb_size as u64;
let dblks = (0..ndblks)
.map(|k| plan_dblk(&mut cursor, first + k * nelmts, nelmts))
.collect();
supers.push((Some(sb_addr), u, dblks));
}
let slot = |i: usize| slots.get(i).and_then(Option::as_ref);
let write_length = |buf: &mut Vec<u8>, val: u64| match length_size {
4 => buf.extend_from_slice(&(val as u32).to_le_bytes()),
_ => buf.extend_from_slice(&val.to_le_bytes()),
};
let write_addr = |buf: &mut Vec<u8>, val: u64| match offset_size {
4 => buf.extend_from_slice(&(val as u32).to_le_bytes()),
_ => buf.extend_from_slice(&val.to_le_bytes()),
let write_addr_opt = |buf: &mut Vec<u8>, addr: Option<u64>| match addr {
Some(a) => push_addr(buf, a, offset_size),
None => buf.extend(core::iter::repeat_n(0xFF, os)),
};
let block_prefix = |buf: &mut Vec<u8>, sig: &[u8; 4], block_off: usize| {
buf.extend_from_slice(sig);
buf.push(0); // version
buf.push(client_id);
push_addr(buf, ea_base_address, offset_size);
buf.extend_from_slice(&(block_off as u64).to_le_bytes()[..arr_off_size]);
};
// Serialise one data block (paged or not) onto `out`.
let write_dblk = |out: &mut Vec<u8>, db: &DataBlock| {
let at = out.len();
block_prefix(out, b"EADB", db.start);
let first = idx_blk + db.start;
if db.nelmts > page_nelmts {
// Paged: the prefix carries only its own checksum; each page
// follows with one of its own.
let sum = jenkins_lookup3(&out[at..]);
out.extend_from_slice(&sum.to_le_bytes());
for p in 0..db.nelmts / page_nelmts {
let page_at = out.len();
for e in 0..page_nelmts {
let i = first + p * page_nelmts + e;
push_index_element(out, slot(i), offset_size, chunk_size_bytes);
}
let sum = jenkins_lookup3(&out[page_at..]);
out.extend_from_slice(&sum.to_le_bytes());
}
} else {
for i in first..first + db.nelmts {
push_index_element(out, slot(i), offset_size, chunk_size_bytes);
}
let sum = jenkins_lookup3(&out[at..]);
out.extend_from_slice(&sum.to_le_bytes());
}
debug_assert_eq!(out.len() - at, dblk_size(db.nelmts));
};
write_length(&mut aehd, 0);
write_length(&mut aehd, 0);
write_length(&mut aehd, n_active_dblks);
write_length(&mut aehd, data_blk_total_size);
write_length(&mut aehd, num_elements as u64);
write_length(&mut aehd, max_idx_set);
// Header (EAHD). The six statistics are, in order: super blocks, their
// bytes, data blocks, their bytes, max index set, elements realised.
let mut out = Vec::with_capacity((cursor - ea_base_address) as usize);
out.extend_from_slice(b"EAHD");
out.push(0); // version
out.push(client_id);
out.push(elem_size as u8);
out.push(MAX_NELMTS_BITS);
out.push(IDX_BLK_ELMTS);
out.push(DATA_BLK_MIN_ELMTS);
out.push(SUP_BLK_MIN_DATA_PTRS);
out.push(MAX_DBLK_PAGE_NELMTS_BITS);
write_length(&mut out, nsuper_blks);
write_length(&mut out, super_blk_size);
write_length(&mut out, ndata_blks);
write_length(&mut out, data_blk_size);
write_length(&mut out, max_idx_set as u64);
write_length(&mut out, realized);
push_addr(&mut out, aeib_address, offset_size);
let sum = jenkins_lookup3(&out);
out.extend_from_slice(&sum.to_le_bytes());
debug_assert_eq!(out.len(), aehd_size);
write_addr(&mut aehd, aeib_address);
let aehd_checksum = jenkins_lookup3(&aehd);
aehd.extend_from_slice(&aehd_checksum.to_le_bytes());
debug_assert_eq!(aehd.len(), aehd_size);
// Build AEIB
let mut aeib = Vec::with_capacity(aeib_size);
aeib.extend_from_slice(b"EAIB");
aeib.push(0);
aeib.push(client_id);
match offset_size {
4 => aeib.extend_from_slice(&(ea_base_address as u32).to_le_bytes()),
8 => aeib.extend_from_slice(&ea_base_address.to_le_bytes()),
_ => aeib.extend_from_slice(&ea_base_address.to_le_bytes()),
// Index block (EAIB): inline elements, data block and super block
// addresses.
let ib_start = out.len();
out.extend_from_slice(b"EAIB");
out.push(0);
out.push(client_id);
push_addr(&mut out, ea_base_address, offset_size);
for i in 0..idx_blk {
push_index_element(&mut out, slot(i), offset_size, chunk_size_bytes);
}
// Inline elements
#[allow(clippy::needless_range_loop)]
for i in 0..idx_blk_elmts as usize {
if i < n_inline {
write_chunk_element(
&mut aeib,
&chunks[i],
offset_size,
has_filters,
chunk_size_bytes,
);
} else {
write_undefined_element(&mut aeib, offset_size, has_filters, chunk_size_bytes);
for db in &direct {
write_addr_opt(&mut out, db.addr);
}
for (sb_addr, _, _) in &supers {
write_addr_opt(&mut out, *sb_addr);
}
let sum = jenkins_lookup3(&out[ib_start..]);
out.extend_from_slice(&sum.to_le_bytes());
debug_assert_eq!(out.len() - ib_start, aeib_size);
// Data block addresses + build data blocks
let mut data_blocks_buf = Vec::new();
let dblks_base = aeib_address + aeib_size as u64;
let mut dblk_cursor = dblks_base;
let mut chunk_idx = n_inline;
for &nelmts in &dblk_sizes {
if chunk_idx >= num_elements {
match offset_size {
4 => aeib.extend_from_slice(&u32::MAX.to_le_bytes()),
8 => aeib.extend_from_slice(&u64::MAX.to_le_bytes()),
_ => aeib.extend_from_slice(&u64::MAX.to_le_bytes()),
for db in direct.iter().filter(|d| d.addr.is_some()) {
write_dblk(&mut out, db);
}
for (sb_addr, u, dblks) in &supers {
if sb_addr.is_none() {
continue;
}
match offset_size {
4 => aeib.extend_from_slice(&(dblk_cursor as u32).to_le_bytes()),
8 => aeib.extend_from_slice(&dblk_cursor.to_le_bytes()),
_ => aeib.extend_from_slice(&dblk_cursor.to_le_bytes()),
}
// Build EADB
let mut aedb = Vec::new();
aedb.extend_from_slice(b"EADB");
aedb.push(0);
aedb.push(client_id);
match offset_size {
4 => aedb.extend_from_slice(&(ea_base_address as u32).to_le_bytes()),
8 => aedb.extend_from_slice(&ea_base_address.to_le_bytes()),
_ => aedb.extend_from_slice(&ea_base_address.to_le_bytes()),
}
let blk_off_size = (max_nelmts_bits as usize).div_ceil(8);
let blk_off_val = (chunk_idx - n_inline) as u64;
aedb.extend_from_slice(&blk_off_val.to_le_bytes()[..blk_off_size]);
for slot in 0..nelmts {
if chunk_idx + slot < num_elements {
write_chunk_element(
&mut aedb,
&chunks[chunk_idx + slot],
offset_size,
has_filters,
chunk_size_bytes,
);
} else {
write_undefined_element(&mut aedb, offset_size, has_filters, chunk_size_bytes);
let (ndblks, nelmts, first) = levels[*u];
let sb_start = out.len();
block_prefix(&mut out, b"EASB", first);
if nelmts > page_nelmts {
// Page-init bits, `npages` per data block, packed MSB-first
// (`H5VM_bit_set`): every page of an allocated data block is
// written.
let npages = nelmts / page_nelmts;
let mut bitmap = vec![0u8; sblk_bitmap_len(ndblks, nelmts)];
for (k, db) in dblks.iter().enumerate() {
if db.addr.is_some() {
for p in 0..npages {
let bit = k * npages + p;
bitmap[bit / 8] |= 0x80 >> (bit % 8);
}
}
let aedb_checksum = jenkins_lookup3(&aedb);
aedb.extend_from_slice(&aedb_checksum.to_le_bytes());
dblk_cursor += aedb.len() as u64;
data_blocks_buf.extend_from_slice(&aedb);
chunk_idx += nelmts;
}
// Super block addresses (all undefined)
for _ in 0..n_sblk_addrs {
match offset_size {
4 => aeib.extend_from_slice(&u32::MAX.to_le_bytes()),
8 => aeib.extend_from_slice(&u64::MAX.to_le_bytes()),
_ => aeib.extend_from_slice(&u64::MAX.to_le_bytes()),
out.extend_from_slice(&bitmap);
}
for db in dblks {
write_addr_opt(&mut out, db.addr);
}
let sum = jenkins_lookup3(&out[sb_start..]);
out.extend_from_slice(&sum.to_le_bytes());
for db in dblks.iter().filter(|d| d.addr.is_some()) {
write_dblk(&mut out, db);
}
}
let aeib_checksum = jenkins_lookup3(&aeib);
aeib.extend_from_slice(&aeib_checksum.to_le_bytes());
debug_assert_eq!(aeib.len(), aeib_size);
let mut combined = aehd;
combined.extend_from_slice(&aeib);
combined.extend_from_slice(&data_blocks_buf);
combined
}
fn write_chunk_element(
buf: &mut Vec<u8>,
chunk: &WrittenChunk,
offset_size: u8,
has_filters: bool,
chunk_size_bytes: usize,
) {
match offset_size {
4 => buf.extend_from_slice(&(chunk.address as u32).to_le_bytes()),
8 => buf.extend_from_slice(&chunk.address.to_le_bytes()),
_ => buf.extend_from_slice(&chunk.address.to_le_bytes()),
}
if has_filters {
let cs_bytes = chunk.compressed_size.to_le_bytes();
buf.extend_from_slice(&cs_bytes[..chunk_size_bytes]);
buf.extend_from_slice(&chunk.filter_mask.to_le_bytes());
}
}
fn write_undefined_element(
buf: &mut Vec<u8>,
offset_size: u8,
has_filters: bool,
chunk_size_bytes: usize,
) {
let os = offset_size as usize;
// Use extend with repeat to avoid heap-allocating a temporary Vec on each call.
buf.extend(core::iter::repeat_n(0xFF, os));
if has_filters {
buf.extend(core::iter::repeat_n(0x00, chunk_size_bytes));
buf.extend_from_slice(&0u32.to_le_bytes());
}
debug_assert_eq!(out.len() as u64, cursor - ea_base_address);
out
}
+24
View File
@@ -80,6 +80,9 @@ pub enum FormatError {
InvalidLocalHeapSignature,
/// Invalid local heap version.
InvalidLocalHeapVersion(u8),
/// A local heap's free list points outside its data segment (libhdf5:
/// "bad heap free list").
InvalidLocalHeapFreeList,
/// Invalid B-tree v1 signature.
InvalidBTreeSignature,
/// Invalid B-tree node type.
@@ -117,6 +120,14 @@ pub enum FormatError {
/// A message is marked shared but was parsed without access to the file,
/// so the reference to the real message could not be followed.
UnresolvedSharedMessage,
/// A shared-message reference points at an object header that holds no
/// (unshared) message of the referenced type (raw message type id).
SharedMessageTargetMissing(u16),
/// A superblock was parsed at a non-zero offset of the buffer (the file
/// has a user block of this many bytes). HDF5 addresses are relative to
/// the superblock, so the buffer must start there: see
/// `signature::split_user_block`.
UserBlockNotStripped(u64),
/// A selection does not fit the dataset it was applied to (wrong rank, or
/// it reaches past a dimension's extent).
SelectionOutOfBounds(String),
@@ -270,6 +281,9 @@ impl fmt::Display for FormatError {
FormatError::InvalidLocalHeapSignature => {
write!(f, "invalid local heap signature")
}
FormatError::InvalidLocalHeapFreeList => {
write!(f, "bad local heap free list")
}
FormatError::InvalidLocalHeapVersion(v) => {
write!(f, "invalid local heap version: {v}")
}
@@ -339,6 +353,16 @@ impl fmt::Display for FormatError {
FormatError::SelectionOutOfBounds(msg) => {
write!(f, "selection out of bounds: {msg}")
}
FormatError::UserBlockNotStripped(n) => write!(
f,
"file has a {n}-byte user block: parse the bytes from the superblock on \
(signature::split_user_block)"
),
FormatError::SharedMessageTargetMissing(t) => write!(
f,
"shared message reference points at an object header with no message of type \
{t:#06x}"
),
FormatError::UnresolvedSharedMessage => write!(
f,
"message is shared but no file data was available to resolve it"
+56 -99
View File
@@ -9,6 +9,7 @@ extern crate alloc;
#[cfg(not(feature = "std"))]
use alloc::{format, vec, vec::Vec};
use crate::chunk_grid::ChunkGrid;
use crate::chunked_read::ChunkInfo;
use crate::error::FormatError;
@@ -203,8 +204,7 @@ fn read_element(
offset_size: u8,
chunk_byte_size: u64,
linear_index: usize,
num_chunks_per_dim: &[u64],
chunk_dimensions: &[u32],
grid: &ChunkGrid,
) -> Result<(Option<ChunkInfo>, usize), FormatError> {
let os = offset_size as usize;
@@ -220,7 +220,10 @@ fn read_element(
return Ok((None, os));
}
let address = read_offset(data, pos, offset_size)?;
let offsets = index_to_chunk_offsets(linear_index, num_chunks_per_dim, chunk_dimensions);
// A slot beyond the current extent is ignored, as the library does.
let Some(offsets) = grid.offsets(linear_index as u64) else {
return Ok((None, os));
};
Ok((
Some(ChunkInfo {
chunk_size: chunk_byte_size as u32,
@@ -261,7 +264,9 @@ fn read_element(
data[fm_off + 2],
data[fm_off + 3],
]);
let offsets = index_to_chunk_offsets(linear_index, num_chunks_per_dim, chunk_dimensions);
let Some(offsets) = grid.offsets(linear_index as u64) else {
return Ok((None, elem_total));
};
Ok((
Some(ChunkInfo {
chunk_size: chunk_size as u32,
@@ -274,27 +279,6 @@ fn read_element(
}
}
/// Convert a linear chunk index to N-dimensional chunk offsets in dataset space.
fn index_to_chunk_offsets(
index: usize,
num_chunks_per_dim: &[u64],
chunk_dimensions: &[u32],
) -> Vec<u64> {
let rank = num_chunks_per_dim.len();
let mut offsets = vec![0u64; rank];
let mut remaining = index as u64;
for d in (0..rank).rev() {
let nchunks = num_chunks_per_dim[d];
if nchunks == 0 {
continue;
}
let chunk_idx = remaining % nchunks;
remaining /= nchunks;
offsets[d] = chunk_idx * chunk_dimensions[d] as u64;
}
offsets
}
/// Collect elements from a data block at the given offset.
#[allow(clippy::too_many_arguments)]
/// Layout of super block `u`, per the HDF5 spec: the number of data blocks it
@@ -339,8 +323,7 @@ fn read_data_block_elements(
offset_size: u8,
chunk_byte_size: u64,
start_index: usize,
num_chunks_per_dim: &[u64],
chunk_dimensions: &[u32],
grid: &ChunkGrid,
page_init: &[u8],
first_page: usize,
) -> Result<Vec<ChunkInfo>, FormatError> {
@@ -376,8 +359,7 @@ fn read_data_block_elements(
offset_size,
chunk_byte_size,
first_index + i,
num_chunks_per_dim,
chunk_dimensions,
grid,
)?;
if let Some(ci) = info {
chunks.push(ci);
@@ -449,25 +431,19 @@ pub fn read_extensible_array_chunks(
file_data: &[u8],
header: &ExtensibleArrayHeader,
dataset_dims: &[u64],
max_dims: Option<&[u64]>,
chunk_dimensions: &[u32],
element_size: u32,
offset_size: u8,
_length_size: u8,
) -> Result<Vec<ChunkInfo>, FormatError> {
let rank = chunk_dimensions.len();
let os = offset_size as usize;
let mut num_chunks_per_dim = Vec::with_capacity(rank);
for d in 0..rank {
let ch_dim = chunk_dimensions[d] as u64;
if ch_dim == 0 {
return Err(FormatError::ChunkedReadError(
"chunk dimension is zero".into(),
));
}
let ds_dim = dataset_dims[d];
num_chunks_per_dim.push(ds_dim.div_ceil(ch_dim));
}
// Linear indexes follow the maximum dimensions, with the unlimited
// dimension swizzled to the slowest position (see `chunk_grid`).
let dims_u64: Vec<u64> = chunk_dimensions.iter().map(|&d| d as u64).collect();
let grid = ChunkGrid::extensible_array(dataset_dims, max_dims, &dims_u64)?;
let grid = &grid;
let chunk_byte_size: u64 =
chunk_dimensions.iter().map(|&d| d as u64).product::<u64>() * element_size as u64;
@@ -557,8 +533,7 @@ pub fn read_extensible_array_chunks(
offset_size,
chunk_byte_size,
i,
&num_chunks_per_dim,
chunk_dimensions,
grid,
)?;
if let Some(ci) = info {
chunks.push(ci);
@@ -594,8 +569,7 @@ pub fn read_extensible_array_chunks(
offset_size,
chunk_byte_size,
global_index,
&num_chunks_per_dim,
chunk_dimensions,
grid,
&[],
0,
)?);
@@ -625,8 +599,7 @@ pub fn read_extensible_array_chunks(
offset_size,
chunk_byte_size,
global_index,
&num_chunks_per_dim,
chunk_dimensions,
grid,
)?);
}
global_index =
@@ -653,8 +626,7 @@ fn read_super_block(
offset_size: u8,
chunk_byte_size: u64,
start_index: usize,
num_chunks_per_dim: &[u64],
chunk_dimensions: &[u32],
grid: &ChunkGrid,
) -> Result<Vec<ChunkInfo>, FormatError> {
let os = offset_size as usize;
let sb_header_size = 4 + 1 + 1 + os + arr_off_size(header);
@@ -710,8 +682,7 @@ fn read_super_block(
offset_size,
chunk_byte_size,
global_idx,
num_chunks_per_dim,
chunk_dimensions,
grid,
bitmap,
i * npages,
)?);
@@ -735,35 +706,18 @@ mod tests {
}
#[test]
fn index_to_offsets_1d() {
let num_chunks = vec![5u64];
let chunk_dims = vec![20u32];
assert_eq!(index_to_chunk_offsets(0, &num_chunks, &chunk_dims), vec![0]);
assert_eq!(
index_to_chunk_offsets(1, &num_chunks, &chunk_dims),
vec![20]
);
assert_eq!(
index_to_chunk_offsets(4, &num_chunks, &chunk_dims),
vec![80]
);
let g = ChunkGrid::fixed_array(&[100], None, &[20]).unwrap();
assert_eq!(g.offsets(0).unwrap(), vec![0]);
assert_eq!(g.offsets(1).unwrap(), vec![20]);
assert_eq!(g.offsets(4).unwrap(), vec![80]);
}
#[test]
fn index_to_offsets_2d() {
let num_chunks = vec![3u64, 2];
let chunk_dims = vec![4u32, 3];
assert_eq!(
index_to_chunk_offsets(0, &num_chunks, &chunk_dims),
vec![0, 0]
);
assert_eq!(
index_to_chunk_offsets(1, &num_chunks, &chunk_dims),
vec![0, 3]
);
assert_eq!(
index_to_chunk_offsets(2, &num_chunks, &chunk_dims),
vec![4, 0]
);
let g = ChunkGrid::fixed_array(&[10, 6], None, &[4, 3]).unwrap();
assert_eq!(g.offsets(0).unwrap(), vec![0, 0]);
assert_eq!(g.offsets(1).unwrap(), vec![0, 3]);
assert_eq!(g.offsets(2).unwrap(), vec![4, 0]);
}
#[test]
@@ -830,7 +784,7 @@ mod tests {
index_block_address: (usize::MAX - 4) as u64,
};
let buf = vec![0u8; 64];
let r = read_extensible_array_chunks(&buf, &header, &[100], &[20], 8, 8, 8);
let r = read_extensible_array_chunks(&buf, &header, &[100], None, &[20], 8, 8, 8);
assert!(r.is_err());
}
@@ -913,8 +867,16 @@ mod tests {
let header = ExtensibleArrayHeader::parse(&file_data, aehd_offset, os, ls).unwrap();
let ds_dims = vec![40u64]; // 2 chunks × 20 elements
let chunk_dims = vec![20u32];
let chunks =
read_extensible_array_chunks(&file_data, &header, &ds_dims, &chunk_dims, 8, os, ls)
let chunks = read_extensible_array_chunks(
&file_data,
&header,
&ds_dims,
None,
&chunk_dims,
8,
os,
ls,
)
.unwrap();
assert_eq!(chunks.len(), 2);
@@ -1023,8 +985,16 @@ mod tests {
let header = ExtensibleArrayHeader::parse(&file_data, aehd_offset, os, ls).unwrap();
let ds_dims = vec![40u64];
let chunk_dims = vec![10u32];
let chunks =
read_extensible_array_chunks(&file_data, &header, &ds_dims, &chunk_dims, 8, os, ls)
let chunks = read_extensible_array_chunks(
&file_data,
&header,
&ds_dims,
None,
&chunk_dims,
8,
os,
ls,
)
.unwrap();
assert_eq!(chunks.len(), 4);
@@ -1047,10 +1017,8 @@ mod tests {
#[test]
fn read_element_unallocated() {
let data = vec![0xFFu8; 16];
let num_chunks = vec![5u64];
let chunk_dims = vec![10u32];
let (info, consumed) =
read_element(&data, 0, 0, 8, 8, 80, 0, &num_chunks, &chunk_dims).unwrap();
let grid = ChunkGrid::fixed_array(&[50], None, &[10]).unwrap();
let (info, consumed) = read_element(&data, 0, 0, 8, 8, 80, 0, &grid).unwrap();
assert!(info.is_none());
assert_eq!(consumed, 8);
}
@@ -1069,20 +1037,9 @@ mod tests {
// Filter mask
data[12..16].copy_from_slice(&0u32.to_le_bytes());
let num_chunks = vec![5u64];
let chunk_dims = vec![10u32];
let (info, consumed) = read_element(
&data,
0,
1,
elem_size as u8,
os,
80,
2,
&num_chunks,
&chunk_dims,
)
.unwrap();
let grid = ChunkGrid::fixed_array(&[50], None, &[10]).unwrap();
let (info, consumed) =
read_element(&data, 0, 1, elem_size as u8, os, 80, 2, &grid).unwrap();
let ci = info.unwrap();
assert_eq!(ci.address, 0x2000);
assert_eq!(ci.chunk_size, 120);
+201 -59
View File
@@ -4,7 +4,7 @@
//! link messages, contiguous datasets, inline and dense attributes.
#[cfg(not(feature = "std"))]
use alloc::{string::String, string::ToString, vec, vec::Vec};
use alloc::{format, string::String, string::ToString, vec, vec::Vec};
use crate::attribute::AttributeMessage;
use crate::chunked_write::{
@@ -19,7 +19,7 @@ use crate::metadata_index::{DatasetMetadata, MetadataBlock, MetadataIndex};
use crate::object_header_writer::ObjectHeaderWriter;
use crate::superblock::Superblock;
use crate::type_builders::{
DatasetBuilder, FillTime, FinishedGroup, GroupBuilder, build_attr_message,
DatasetBuilder, FinishedGroup, GroupBuilder, build_attr_message, fill_value_message,
};
// Re-export public types that moved to type_builders for API compatibility.
@@ -33,6 +33,49 @@ pub(crate) const OFFSET_SIZE: u8 = 8;
pub(crate) const LENGTH_SIZE: u8 = 8;
const SUPERBLOCK_SIZE: usize = 48;
/// Largest raw data a compact dataset can hold: the layout message (version,
/// class, 2-byte size, data) must fit an object header message, whose size
/// field is 2 bytes. Bigger "compact" requests fall back to contiguous storage.
const MAX_COMPACT_DATA_SIZE: usize = crate::object_header_writer::MAX_MESSAGE_SIZE - 4;
/// libhdf5's bounds on a file space page size (`H5F_FILE_SPACE_PAGE_SIZE_MIN`
/// and `_MAX`).
const MIN_FILE_SPACE_PAGE_SIZE: u32 = 512;
const MAX_FILE_SPACE_PAGE_SIZE: u32 = 1024 * 1024 * 1024;
/// Superblock extension object header for a file using the paged file-space
/// strategy: a single File Space Info message (0x0017), as libhdf5 writes it
/// for `fs_strategy="page"` without persisted free space.
fn build_paged_superblock_extension(page_size: u32) -> Result<Vec<u8>, FormatError> {
let mut fsinfo = Vec::new();
fsinfo.push(1); // version
fsinfo.push(1); // strategy: H5F_FSPACE_STRATEGY_PAGE
fsinfo.push(0); // persisting free space: no
write_length(&mut fsinfo, 1, LENGTH_SIZE); // free-space section threshold
write_length(&mut fsinfo, u64::from(page_size), LENGTH_SIZE);
fsinfo.extend_from_slice(&0u16.to_le_bytes()); // page end metadata threshold
write_undef_offset(&mut fsinfo, OFFSET_SIZE); // EOA before free-space info
let mut w = ObjectHeaderWriter::new();
// Flags as libhdf5 sets them: bit 2 (never share) and bit 4 (mark if
// unknown). Not constant: libhdf5 rewrites the message when it closes a
// file it opened for writing.
w.add_message_with_flags(MessageType::Unknown(0x0017), fsinfo, 0x14);
w.serialize()
}
/// A group or dataset name must be one path component: not empty, not ".",
/// and without '/'. `FileWriter` writes a root group plus one level of
/// groups, and cannot create intermediate groups for a path.
fn check_link_name(name: &str) -> Result<(), FormatError> {
if name.is_empty() || name == "." || name.contains('/') {
return Err(FormatError::SerializationError(format!(
"invalid object name {name:?}: names must be a single path component \
(FileWriter does not create nested groups)"
)));
}
Ok(())
}
/// Threshold for switching from compact (inline) to dense attribute storage.
const DENSE_ATTR_THRESHOLD: usize = 8;
@@ -50,12 +93,12 @@ pub(crate) fn build_chunked_dataset_oh(
pipeline_message: Option<&[u8]>,
attrs: &[AttributeMessage],
dense_blob: Option<&DenseAttrBlob>,
fill_time: FillTime,
) -> Vec<u8> {
fill_message: &[u8],
) -> Result<Vec<u8>, FormatError> {
let mut w = ObjectHeaderWriter::new();
w.add_message_with_flags(MessageType::Datatype, dt.serialize(), 0x01);
w.add_message(MessageType::Dataspace, ds.serialize(LENGTH_SIZE));
w.add_message_with_flags(MessageType::FillValue, vec![3, fill_time.to_byte()], 0x01);
w.add_message_with_flags(MessageType::FillValue, fill_message.to_vec(), 0x01);
w.add_message(MessageType::DataLayout, layout_message.to_vec());
if let Some(pm) = pipeline_message {
w.add_message(MessageType::FilterPipeline, pm.to_vec());
@@ -77,15 +120,21 @@ pub(crate) fn build_dataset_oh(
data_size: u64,
attrs: &[AttributeMessage],
dense_blob: Option<&DenseAttrBlob>,
fill_time: FillTime,
) -> Vec<u8> {
fill_message: &[u8],
) -> Result<Vec<u8>, FormatError> {
let mut w = ObjectHeaderWriter::new();
w.add_message_with_flags(MessageType::Datatype, dt.serialize(), 0x01);
w.add_message(MessageType::Dataspace, ds.serialize(LENGTH_SIZE));
w.add_message_with_flags(MessageType::FillValue, vec![3, fill_time.to_byte()], 0x01);
w.add_message_with_flags(MessageType::FillValue, fill_message.to_vec(), 0x01);
let mut dl = Vec::new();
dl.push(4); // version
dl.push(1); // class = contiguous
// An empty dataset has no storage: its address must be the undefined
// address, as libhdf5 writes it. A real address with size 0 trips
// libhdf5's `addr + size <= addr` overflow check, and it refuses the
// dataset as "invalid dataset size, likely file corruption" — which made
// every store with no sessions or knowledge graph unreadable by h5py.
let data_addr = if data_size == 0 { u64::MAX } else { data_addr };
dl.extend_from_slice(&data_addr.to_le_bytes());
dl.extend_from_slice(&data_size.to_le_bytes());
w.add_message(MessageType::DataLayout, dl);
@@ -106,12 +155,12 @@ pub(crate) fn build_compact_dataset_oh(
data: &[u8],
attrs: &[AttributeMessage],
dense_blob: Option<&DenseAttrBlob>,
fill_time: FillTime,
) -> Vec<u8> {
fill_message: &[u8],
) -> Result<Vec<u8>, FormatError> {
let mut w = ObjectHeaderWriter::new();
w.add_message_with_flags(MessageType::Datatype, dt.serialize(), 0x01);
w.add_message(MessageType::Dataspace, ds.serialize(LENGTH_SIZE));
w.add_message_with_flags(MessageType::FillValue, vec![3, fill_time.to_byte()], 0x01);
w.add_message_with_flags(MessageType::FillValue, fill_message.to_vec(), 0x01);
// Compact layout message: version=4, class=0, u16 size, inline data
let mut dl = Vec::new();
dl.push(4); // version
@@ -134,7 +183,7 @@ pub(crate) fn build_group_oh(
dense_link_info: Option<&[u8]>,
attrs: &[AttributeMessage],
dense_blob: Option<&DenseAttrBlob>,
) -> Vec<u8> {
) -> Result<Vec<u8>, FormatError> {
let mut w = ObjectHeaderWriter::new();
if let Some(li) = dense_link_info {
// Dense link storage: a LinkInfo pointing at the fractal heap + name
@@ -896,12 +945,12 @@ pub(crate) fn build_vds_dataset_oh(
global_heap_addr: u64,
attrs: &[AttributeMessage],
dense_blob: Option<&DenseAttrBlob>,
fill_time: FillTime,
) -> Vec<u8> {
fill_message: &[u8],
) -> Result<Vec<u8>, FormatError> {
let mut w = ObjectHeaderWriter::new();
w.add_message_with_flags(MessageType::Datatype, dt.serialize(), 0x01);
w.add_message(MessageType::Dataspace, ds.serialize(LENGTH_SIZE));
w.add_message_with_flags(MessageType::FillValue, vec![3, fill_time.to_byte()], 0x01);
w.add_message_with_flags(MessageType::FillValue, fill_message.to_vec(), 0x01);
// VDS layout message: version=4, class=3, global_heap_address(8), global_heap_index=1(4)
let mut dl = Vec::new();
dl.push(4u8); // version
@@ -950,7 +999,9 @@ pub struct FileWriter {
alignment_threshold: usize,
/// Global alignment boundary in bytes (0 = disabled).
alignment_bytes: usize,
/// Page size for page-buffer mode. When set, a v4 superblock is written.
/// File space page size. When set, the file uses libhdf5's paged
/// file-space strategy (a File Space Info message in the superblock
/// extension).
page_size: Option<u32>,
}
@@ -982,9 +1033,16 @@ impl FileWriter {
self
}
/// Enable page-buffer mode with the given page size. Writing this causes
/// the file to be written with a v4 superblock (page_size field) instead
/// of the default v3.
/// Write the file with libhdf5's *paged* file-space strategy and the given
/// page size, as `H5Pset_file_space_strategy(H5F_FSPACE_STRATEGY_PAGE)` +
/// `H5Pset_file_space_page_size` (h5py: `fs_strategy="page"`,
/// `fs_page_size=...`) do: a v3 superblock with an extension holding a
/// File Space Info message, and the file padded to a whole number of
/// pages. Readers with a page buffer can then fetch metadata page by page.
///
/// `page_size` must be between 512 bytes and 1 GiB (libhdf5's limits);
/// [`Self::finish`] fails otherwise. This used to write a "version 4"
/// superblock, which does not exist and no HDF5 library can open.
pub fn with_page_size(&mut self, page_size: u32) -> &mut Self {
self.page_size = Some(page_size);
self
@@ -1009,6 +1067,14 @@ impl FileWriter {
pub fn finish(self) -> Result<Vec<u8>, FormatError> {
let page_size = self.page_size;
if let Some(ps) = page_size
&& !(MIN_FILE_SPACE_PAGE_SIZE..=MAX_FILE_SPACE_PAGE_SIZE).contains(&ps)
{
return Err(FormatError::SerializationError(format!(
"file space page size {ps} is outside libhdf5's \
{MIN_FILE_SPACE_PAGE_SIZE}..={MAX_FILE_SPACE_PAGE_SIZE} bytes"
)));
}
struct DsFlat {
name: String,
dt: Datatype,
@@ -1017,7 +1083,8 @@ impl FileWriter {
attrs: Vec<AttributeMessage>,
chunk_options: ChunkOptions,
maxshape: Option<Vec<u64>>,
fill_time: FillTime,
/// Serialized Fill Value message.
fill_message: Vec<u8>,
compact: bool,
alignment: usize,
/// VDS source mappings (set for Virtual datasets).
@@ -1067,6 +1134,7 @@ impl FileWriter {
};
attrs.extend(p.build_attrs(&raw));
}
let fill_message = fill_value_message(db.fill_time, db.fill_value.as_deref(), &dt)?;
Ok(DsFlat {
name: db.name,
dt,
@@ -1075,13 +1143,26 @@ impl FileWriter {
attrs,
chunk_options: db.chunk_options,
maxshape: db.maxshape,
fill_time: db.fill_time,
fill_message,
compact: db.compact,
alignment: db.alignment,
virtual_sources: db.virtual_sources,
})
};
// Every name becomes a single link in its parent group. The writer
// has no nested groups, so a path like "a/b" would be stored as one
// link literally named "a/b" — which no HDF5 reader can resolve.
let root_names = self.root_datasets.iter().map(|d| d.name.as_str());
let group_names = self.groups.iter().flat_map(|g| {
core::iter::once(g.name.as_str())
.chain(g.datasets.iter().map(|d| d.name.as_str()))
.chain(g.external_links.iter().map(|l| l.0.as_str()))
});
for name in root_names.chain(group_names) {
check_link_name(name)?;
}
let mut all_ds: Vec<DsFlat> = Vec::new();
let mut groups: Vec<GrpFlat> = Vec::new();
let mut root_ds_indices: Vec<usize> = Vec::new();
@@ -1114,17 +1195,35 @@ impl FileWriter {
root_attrs.push(build_attr_message(n, v));
}
// Every datatype must have an on-disk encoding before anything is laid
// out: `Datatype::serialize` itself cannot report a failure.
let group_attrs = groups.iter().flat_map(|g| &g.attrs);
let ds_attrs = all_ds.iter().flat_map(|d| &d.attrs);
for a in root_attrs.iter().chain(group_attrs).chain(ds_attrs) {
a.datatype.check_encodable()?;
}
for d in &all_ds {
d.dt.check_encodable()?;
}
let is_vds: Vec<bool> = all_ds.iter().map(|d| d.virtual_sources.is_some()).collect();
let is_chunked: Vec<bool> = all_ds
.iter()
.enumerate()
.map(|(i, d)| !is_vds[i] && (d.chunk_options.is_chunked() || d.maxshape.is_some()))
.map(|(i, d)| {
// Only a dataset that can grow needs chunks; a maxshape equal
// to the shape is as fixed as no maxshape at all.
let resizable = d.maxshape.as_ref().is_some_and(|m| *m != d.ds.dimensions);
!is_vds[i] && (d.chunk_options.is_chunked() || resizable)
})
.collect();
// Determine which datasets use compact storage
let is_compact: Vec<bool> = all_ds
.iter()
.enumerate()
.map(|(i, d)| !is_vds[i] && !is_chunked[i] && d.compact && d.raw.len() <= 65535)
.map(|(i, d)| {
!is_vds[i] && !is_chunked[i] && d.compact && d.raw.len() <= MAX_COMPACT_DATA_SIZE
})
.collect();
let root_dense = root_attrs.len() > DENSE_ATTR_THRESHOLD;
let group_dense: Vec<bool> = groups
@@ -1163,9 +1262,9 @@ impl FileWriter {
}
let attr_blob = group_dense[gi].then(|| build_dense_attrs(&g.attrs, 0));
let dl = group_links_dense[gi].then_some(dummy_link_info.as_slice());
build_group_oh(&dummy_links, dl, &g.attrs, attr_blob.as_ref()).len()
build_group_oh(&dummy_links, dl, &g.attrs, attr_blob.as_ref()).map(|oh| oh.len())
})
.collect();
.collect::<Result<_, _>>()?;
let root_dummy_links: Vec<LinkMessage> = {
let mut links = Vec::new();
@@ -1180,7 +1279,7 @@ impl FileWriter {
let root_oh_size = {
let attr_blob = root_dense.then(|| build_dense_attrs(&root_attrs, 0));
let dl = root_links_dense.then_some(dummy_link_info.as_slice());
build_group_oh(&root_dummy_links, dl, &root_attrs, attr_blob.as_ref()).len()
build_group_oh(&root_dummy_links, dl, &root_attrs, attr_blob.as_ref())?.len()
};
struct DataBlob {
@@ -1208,8 +1307,8 @@ impl FileWriter {
0, // dummy address
&d.attrs,
dense_blob.as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
// Global heap blob size is address-independent; compute it now
// so pass 2 can place it correctly.
let vds_mappings = d.virtual_sources.as_deref().unwrap_or(&[]);
@@ -1238,7 +1337,7 @@ impl FileWriter {
&pre,
dummy_cursor,
d.maxshape.as_deref(),
);
)?;
dummy_cursor += result.data_bytes.len() as u64;
let dense_blob = if ds_dense[i] {
Some(build_dense_attrs(&d.attrs, 0))
@@ -1252,8 +1351,8 @@ impl FileWriter {
result.pipeline_message.as_deref(),
&d.attrs,
dense_blob.as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
dummy_blobs.push(DataBlob {
data: result.data_bytes,
oh_bytes: oh,
@@ -1271,8 +1370,8 @@ impl FileWriter {
&d.raw,
&d.attrs,
dense_blob.as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
dummy_blobs.push(DataBlob {
data: vec![],
oh_bytes: oh,
@@ -1291,8 +1390,8 @@ impl FileWriter {
d.raw.len() as u64,
&d.attrs,
dense_blob.as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
dummy_blobs.push(DataBlob {
data: d.raw.clone(),
oh_bytes: oh,
@@ -1304,12 +1403,12 @@ impl FileWriter {
let actual_ds_oh_sizes: Vec<usize> = dummy_blobs.iter().map(|b| b.oh_bytes.len()).collect();
// Pass 2: compute real addresses
// v4 superblocks add a 4-byte page_size field before the checksum.
let superblock_size = if page_size.is_some() {
SUPERBLOCK_SIZE + 4
} else {
SUPERBLOCK_SIZE
};
// A paged file carries its File Space Info in a superblock extension
// object header, placed right after the superblock.
let sb_ext = page_size
.map(build_paged_superblock_extension)
.transpose()?;
let superblock_size = SUPERBLOCK_SIZE + sb_ext.as_ref().map_or(0, Vec::len);
let root_group_addr = superblock_size as u64;
let mut cursor2 = superblock_size + root_oh_size;
@@ -1400,8 +1499,8 @@ impl FileWriter {
heap_addr,
&d.attrs,
ds_dense_blobs[i].as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
ds_blobs2.push(DataBlob {
data: gcol_bytes.clone(),
oh_bytes: oh,
@@ -1418,7 +1517,7 @@ impl FileWriter {
.expect("chunked dataset missing precompressed cache"),
base_address,
d.maxshape.as_deref(),
);
)?;
cursor2 += result.data_bytes.len();
let oh = build_chunked_dataset_oh(
&d.dt,
@@ -1427,8 +1526,8 @@ impl FileWriter {
result.pipeline_message.as_deref(),
&d.attrs,
ds_dense_blobs[i].as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
ds_blobs2.push(DataBlob {
data: result.data_bytes,
oh_bytes: oh,
@@ -1442,8 +1541,8 @@ impl FileWriter {
&d.raw,
&d.attrs,
ds_dense_blobs[i].as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
ds_blobs2.push(DataBlob {
data: vec![],
oh_bytes: oh,
@@ -1467,8 +1566,8 @@ impl FileWriter {
d.raw.len() as u64,
&d.attrs,
ds_dense_blobs[i].as_ref(),
d.fill_time,
);
&d.fill_message,
)?;
let mut data = vec![0u8; padding];
data.extend_from_slice(&d.raw);
cursor2 += d.raw.len();
@@ -1483,11 +1582,16 @@ impl FileWriter {
let actual_ds_oh_sizes2: Vec<usize> = ds_blobs2.iter().map(|b| b.oh_bytes.len()).collect();
debug_assert_eq!(actual_ds_oh_sizes, actual_ds_oh_sizes2);
// libhdf5 ends a paged file on a page boundary.
let data_end = cursor2;
if let Some(ps) = page_size {
cursor2 = cursor2.next_multiple_of(ps as usize);
}
let eof_addr2 = cursor2 as u64;
let mut buf = Vec::with_capacity(cursor2);
let sb = Superblock {
version: if page_size.is_some() { 4 } else { 3 },
version: 3,
offset_size: OFFSET_SIZE,
length_size: LENGTH_SIZE,
base_address: 0,
@@ -1499,11 +1603,18 @@ impl FileWriter {
free_space_address: None,
driver_info_address: None,
consistency_flags: 0,
superblock_extension_address: Some(u64::MAX),
superblock_extension_address: Some(if sb_ext.is_some() {
SUPERBLOCK_SIZE as u64
} else {
u64::MAX
}),
checksum: None,
page_size,
page_size: None,
};
buf.extend_from_slice(&sb.serialize());
if let Some(ref ext) = sb_ext {
buf.extend_from_slice(ext);
}
// Root group OH
let mut root_links: Vec<LinkMessage> = Vec::new();
@@ -1524,7 +1635,7 @@ impl FileWriter {
root_dl,
&root_attrs,
root_dense_blob.as_ref(),
));
)?);
if let Some(ref b) = root_link_blob {
buf.extend_from_slice(&b.blob);
}
@@ -1549,7 +1660,7 @@ impl FileWriter {
dl,
&g.attrs,
group_dense_blobs[gi].as_ref(),
));
)?);
if let Some(ref b) = link_blob {
buf.extend_from_slice(&b.blob);
}
@@ -1571,7 +1682,8 @@ impl FileWriter {
buf.extend_from_slice(&blob.data);
}
debug_assert_eq!(buf.len(), cursor2);
debug_assert_eq!(buf.len(), data_end);
buf.resize(cursor2, 0);
Ok(buf)
}
}
@@ -1835,6 +1947,12 @@ mod tests {
root_block_address: 0,
current_rows_in_root_indirect_block: 0,
managed_objects_count: 0,
huge_btree_address: u64::MAX,
filter_pipeline: None,
root_direct_block_filtered_size: 0,
root_direct_block_filter_mask: 0,
offset_size: 8,
length_size: 8,
};
let (off, len) = fh.decode_managed_id(&id).unwrap();
assert_eq!(off, 100);
@@ -2145,7 +2263,8 @@ mod tests {
}
#[test]
fn file_writer_v4_superblock() {
fn file_writer_paged_file_uses_v3_superblock_and_fsinfo_extension() {
// This used to write superblock "version 4", which does not exist.
let mut fw = FileWriter::new();
fw.with_page_size(4096);
fw.create_dataset("data").with_f64_data(&[1.0, 2.0]);
@@ -2153,8 +2272,31 @@ mod tests {
let sig = signature::find_signature(&bytes).unwrap();
let sb = Superblock::parse(&bytes, sig).unwrap();
assert_eq!(sb.version, 4, "expected superblock v4");
assert_eq!(sb.page_size, Some(4096));
assert_eq!(sb.version, 3);
assert_eq!(sb.superblock_extension_address, Some(48));
assert_eq!(bytes.len() % 4096, 0);
assert_eq!(sb.eof_address, bytes.len() as u64);
let ext = ObjectHeader::parse(&bytes, 48, 8, 8).unwrap();
let fsinfo = &ext.messages[0];
assert_eq!(fsinfo.msg_type, MessageType::Unknown(0x0017));
// Byte-for-byte what HDF5 2.0 writes for fs_strategy="page",
// fs_page_size=4096.
let mut expected = vec![1u8, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0];
expected.extend_from_slice(&4096u64.to_le_bytes());
expected.extend_from_slice(&[0, 0]);
expected.extend_from_slice(&[0xff; 8]);
assert_eq!(fsinfo.data, expected);
assert_eq!(fsinfo.flags, 0x14);
assert_eq!(read_dataset_f64(&bytes, "data"), vec![1.0, 2.0]);
}
#[test]
fn file_writer_rejects_page_sizes_libhdf5_would() {
for ps in [0u32, 511, MAX_FILE_SPACE_PAGE_SIZE + 1] {
let mut fw = FileWriter::new();
fw.with_page_size(ps);
assert!(fw.finish().is_err(), "page size {ps}");
}
}
#[test]
+42 -7
View File
@@ -98,15 +98,50 @@ pub fn parse_fill_value(msg: &HeaderMessage) -> Result<Option<Vec<u8>>, FormatEr
/// The fill value that applies to a dataset given its header messages. The new
/// message wins over the old one when both are present.
///
/// A *shared* fill value message holds only a reference to the real message,
/// which cannot be followed without the file: this returns
/// [`FormatError::UnresolvedSharedMessage`] for one (it used to answer "zeros").
/// Use [`dataset_fill_value_in`] when the file bytes are at hand.
pub fn dataset_fill_value(messages: &[HeaderMessage]) -> Result<Option<Vec<u8>>, FormatError> {
fill_value_from(messages, |_| Err(FormatError::UnresolvedSharedMessage))
}
/// [`dataset_fill_value`] for a dataset in `file_data`, following a shared
/// fill value message to where it lives: another object header, or the
/// file's shared-message (SOHM) heap, as libhdf5 writes it when the file has
/// a SOHM index for fill values.
pub fn dataset_fill_value_in(
file_data: &[u8],
messages: &[HeaderMessage],
offset_size: u8,
length_size: u8,
) -> Result<Option<Vec<u8>>, FormatError> {
fill_value_from(messages, |msg| {
crate::shared_message::message_data_with_sohm(file_data, msg, offset_size, length_size)
.map(|data| data.into_owned())
})
}
fn fill_value_from(
messages: &[HeaderMessage],
resolve_shared: impl Fn(&HeaderMessage) -> Result<Vec<u8>, FormatError>,
) -> Result<Option<Vec<u8>>, FormatError> {
for wanted in [MessageType::FillValue, MessageType::FillValueOld] {
if let Some(msg) = messages.iter().find(|m| m.msg_type == wanted) {
if crate::shared_message::is_shared(msg.flags) {
// A shared fill value is legal but vanishingly rare; treat it
// as the default rather than misparsing the reference.
return Ok(None);
}
if let Some(value) = parse_fill_value(msg)? {
let value = if crate::shared_message::is_shared(msg.flags) {
let data = resolve_shared(msg)?;
parse_fill_value(&HeaderMessage {
msg_type: msg.msg_type,
size: data.len(),
flags: msg.flags & !0x02,
creation_order: msg.creation_order,
data,
})?
} else {
parse_fill_value(msg)?
};
if let Some(value) = value {
return Ok(Some(value));
}
}
@@ -174,7 +209,7 @@ pub fn read_full_with_fill<E: From<FormatError>>(
{
return Err(FormatError::ExternalDataFilesUnsupported.into());
}
let fill = dataset_fill_value(messages)?;
let fill = dataset_fill_value_in(file_data, messages, offset_size, length_size)?;
if !has_storage(layout) {
return Ok(filled_dataset(dataspace, elem_size, fill.as_deref())?);
}
+17 -2
View File
@@ -19,8 +19,23 @@ pub const FILTER_SCALEOFFSET: u16 = 6;
pub const FILTER_LZ4: u16 = 32004;
/// Zstandard compression.
pub const FILTER_ZSTD: u16 = 32015;
/// Pcodec lossless numerical codec (clawhdf5 internal; not yet HDF5-registered).
pub const FILTER_PCODEC: u16 = 32023;
/// Pcodec lossless numerical codec — a **private, unregistered** clawhdf5
/// filter. Pcodec has no ID in the HDF Group's filter registry (checked
/// 2026-09-25, `hdf5_plugins/docs/RegisteredFilterPlugins.md`), so it uses an
/// ID from the registry's testing/private range (256–511). No libhdf5 plugin
/// decodes it: h5py/libhdf5 report the filter as unavailable. Only clawhdf5
/// (with the `pcodec` feature) reads these datasets.
pub const FILTER_PCODEC: u16 = 480;
/// Filter name written with [`FILTER_PCODEC`].
pub const FILTER_PCODEC_NAME: &str = "pcodec (clawhdf5 private)";
/// The ID clawhdf5 up to 2.7.0 wrote pcodec under. It is registered to
/// Granular BitRound (GBR), whose decode is a pass-through, so libhdf5 with
/// that plugin would have returned the compressed bytes as data. Read as
/// pcodec only when the filter is named exactly [`FILTER_PCODEC_LEGACY_NAME`],
/// the name those versions wrote; never written.
pub const FILTER_PCODEC_LEGACY: u16 = 32023;
/// The filter name clawhdf5 up to 2.7.0 wrote with [`FILTER_PCODEC_LEGACY`].
pub const FILTER_PCODEC_LEGACY_NAME: &str = "pcodec";
/// Description of a single filter in a pipeline.
#[derive(Debug, Clone, PartialEq)]
File diff suppressed because it is too large Load Diff
+173 -37
View File
@@ -1,19 +1,39 @@
//! SZIP (libaec Adaptive Entropy Coding) decompression.
//!
//! Gated by the `szip` feature which links against the system libaec library.
//!
//! libhdf5's SZIP filter (`H5Zszip.c`) prefixes each chunk with its
//! uncompressed size and hands the rest to szlib's `SZ_BufftoBuffDecompress`.
//! libaec implements that call (`sz_compat.c`) on top of `aec_buffer_decode`
//! with some reshaping — 32/64-bit samples are coded as byte planes of 8-bit
//! samples, and scanlines that are not a whole number of blocks are padded —
//! which [`szip_decompress`] reproduces so its output matches libhdf5's.
#[cfg(not(feature = "std"))]
use alloc::vec::Vec;
use crate::error::FormatError;
/// Decompress SZIP-compressed data using libaec.
/// `SZ_MSB_OPTION_MASK`: samples are big-endian.
#[cfg(feature = "szip")]
const SZ_MSB_OPTION_MASK: u32 = 16;
/// `SZ_NN_OPTION_MASK`: nearest-neighbour preprocessing.
#[cfg(feature = "szip")]
const SZ_NN_OPTION_MASK: u32 = 32;
/// Decompress one SZIP-filtered chunk.
///
/// `cd` is the HDF5 SZIP filter client data (matches `H5Z_SZIP_PARM_*` indices):
/// cd[0] = options mask (`H5_SZIP_NN_OPTION_MASK = 0x20` enables NN preprocessing)
/// cd[1] = pixels per block (H5Z_SZIP_PARM_PPB; 8, 10, 16, or 32)
/// cd[2] = bits per sample (H5Z_SZIP_PARM_BPP; element bit width)
/// cd[3] = pixels per scan line (H5Z_SZIP_PARM_PPS; informational only)
/// `cd` is the HDF5 SZIP filter client data (`H5Z_SZIP_PARM_*` indices):
/// cd[0] = options mask (`SZ_*_OPTION_MASK`: 16 = MSB byte order,
/// 32 = nearest-neighbour preprocessing; K13/EC/LSB/RAW bits carry
/// no decoding information for libaec)
/// cd[1] = pixels per block
/// cd[2] = bits per pixel (sample precision, rounded up to 32 or 64 above
/// 24 by libhdf5)
/// cd[3] = pixels per scanline
///
/// The chunk is a 4-byte little-endian uncompressed size followed by the
/// szlib stream.
pub(crate) fn szip_decompress(
_data: &[u8],
_cd: &[u32],
@@ -33,62 +53,174 @@ pub(crate) fn szip_decompress(
#[cfg(feature = "szip")]
fn szip_decode_impl(data: &[u8], cd: &[u32], chunk_size: usize) -> Result<Vec<u8>, FormatError> {
if cd.len() < 3 {
return Err(FormatError::ChunkedReadError(
"szip: missing client data".into(),
));
let err = |m: &str| FormatError::ChunkedReadError(format!("szip: {m}"));
if cd.len() < 4 {
return Err(err("missing client data"));
}
let options = cd[0];
let pixels_per_block = cd[1];
let bits_per_sample = cd[2]; // H5Z_SZIP_PARM_BPP
if bits_per_sample == 0 || bits_per_sample > 32 {
return Err(FormatError::ChunkedReadError(
"szip: invalid bits per sample".into(),
));
let pixels_per_block = cd[1] as usize;
let bits_per_pixel = cd[2];
let pixels_per_scanline = cd[3] as usize;
if !(1..=32).contains(&bits_per_pixel) && bits_per_pixel != 64 {
return Err(err("invalid bits per sample"));
}
if chunk_size == 0 {
return Err(FormatError::ChunkedReadError(
"szip: unknown output size".into(),
));
if pixels_per_block == 0 || pixels_per_scanline == 0 {
return Err(err("invalid block or scanline size"));
}
if data.is_empty() {
return Err(FormatError::ChunkedReadError("szip: empty input".into()));
if data.len() < 4 {
return Err(err("chunk too short"));
}
// H5Zszip.c: UINT32DECODE of the uncompressed size, then the stream.
let dest_len = u32::from_le_bytes([data[0], data[1], data[2], data[3]]) as usize;
let limit = if chunk_size != 0 {
chunk_size
} else {
crate::filters::MAX_DECOMPRESS_SIZE
};
if dest_len > limit {
return Err(err("declared size exceeds chunk size"));
}
let stream = &data[4..];
// Map HDF5 option mask to libaec flags.
// HDF5 always stores SZIP data in MSB order, so AEC_DATA_MSB is unconditional.
// H5_SZIP_NN_OPTION_MASK (0x20): NN differential preprocessing.
let mut flags: u32 = libaec_sys::AEC_DATA_MSB;
if options & 0x20 != 0 {
// --- libaec sz_compat.c: SZ_BufftoBuffDecompress ---
let rsi = pixels_per_scanline.div_ceil(pixels_per_block);
let mut flags = 0;
if options & SZ_MSB_OPTION_MASK != 0 {
flags |= libaec_sys::AEC_DATA_MSB;
}
if options & SZ_NN_OPTION_MASK != 0 {
flags |= libaec_sys::AEC_DATA_PREPROCESS;
}
let pad_scanline = !pixels_per_scanline.is_multiple_of(pixels_per_block);
let deinterleave = bits_per_pixel == 32 || bits_per_pixel == 64;
let bits_per_sample = if deinterleave { 8 } else { bits_per_pixel };
let pixel_size = match bits_per_sample {
17.. => 4,
9.. => 2,
_ => 1,
};
let scanlines = (dest_len / pixel_size).div_ceil(pixels_per_scanline);
let buf_size = if pad_scanline {
rsi.checked_mul(pixels_per_block)
.and_then(|n| n.checked_mul(pixel_size))
.and_then(|n| n.checked_mul(scanlines))
.filter(|&n| n <= crate::filters::MAX_DECOMPRESS_SIZE.max(limit))
.ok_or_else(|| err("scanline padding too large"))?
} else {
dest_len
};
let mut out = vec![0u8; chunk_size];
let mut buf = vec![0u8; buf_size];
let mut strm = libaec_sys::AecStream::zeroed();
strm.next_in = data.as_ptr();
strm.avail_in = data.len();
strm.next_out = out.as_mut_ptr();
strm.avail_out = chunk_size;
strm.next_in = stream.as_ptr();
strm.avail_in = stream.len();
strm.next_out = buf.as_mut_ptr();
strm.avail_out = buf_size;
strm.bits_per_sample = bits_per_sample;
strm.block_size = pixels_per_block;
strm.rsi = 128; // HDF5 default: 128 blocks per reference sample interval
strm.block_size = pixels_per_block as u32;
strm.rsi = rsi as u32;
strm.flags = flags;
// SAFETY: next_in/avail_in and next_out/avail_out describe live buffers
// (`stream` and `buf`) that outlive the call.
let result = unsafe { libaec_sys::aec_buffer_decode(&mut strm) };
if result != 0 {
return Err(FormatError::DecompressionError(format!(
"szip: libaec error {result}"
)));
}
let decoded_len = chunk_size - strm.avail_out;
out.truncate(decoded_len);
let mut total_out = strm.total_out;
if pad_scanline {
let line = pixels_per_scanline * pixel_size;
let padded_line = rsi * pixels_per_block * pixel_size;
// remove_padding: compact each padded line down to `line` bytes.
let mut i = line;
let mut j = padded_line;
while j < total_out {
let end = (j + line).min(buf.len());
buf.copy_within(j..end, i);
i += line;
j += padded_line;
}
total_out = scanlines * line;
}
if total_out < dest_len {
return Err(err("stream decoded to fewer bytes than declared"));
}
buf.truncate(dest_len);
if deinterleave {
// deinterleave_buffer: byte planes back into words.
let w = (bits_per_pixel / 8) as usize;
let n = dest_len / w;
let mut out = vec![0u8; dest_len];
for i in 0..n {
for j in 0..w {
out[i * w + j] = buf[j * n + i];
}
}
Ok(out)
} else {
Ok(buf)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[cfg(feature = "szip")]
fn unhex(s: &str) -> Vec<u8> {
(0..s.len())
.step_by(2)
.map(|i| u8::from_str_radix(&s[i..i + 2], 16).unwrap())
.collect()
}
/// SZIP chunks written by libhdf5, decoded exactly as libhdf5 decodes
/// them. Each case: fixture, chunk byte offset and size (from h5py's
/// `get_chunk_info`), the filter's cd_values, and the chunk's values as
/// h5py reads them (file byte order, hex). Before the fix every one of
/// these came back as garbage or zeros (or "invalid bits per sample" for
/// 64-bit): the 4-byte size prefix was fed to libaec, 32/64-bit samples
/// were not de-interleaved from byte planes, the reference sample
/// interval was fixed at 128 instead of derived from the scanline, padded
/// scanlines were not unpadded, and LE data was decoded as MSB.
#[cfg(feature = "szip")]
#[test]
fn szip_decodes_libhdf5_chunks_exactly() {
/// (name, file, chunk offset, chunk size, cd_values, decoded hex)
type Case<'a> = (&'a str, &'a [u8], usize, usize, [u32; 4], &'a str);
let noencoder: &[u8] = include_bytes!("../tests/fixtures/filters/noencoder.h5");
let le_data: &[u8] = include_bytes!("../tests/fixtures/filters/le_data.h5");
let h5py: &[u8] = include_bytes!("../tests/fixtures/filters/szip_h5py.h5");
#[rustfmt::skip]
let cases: &[Case] = &[
// <i4, 10 px/scanline over 4 px/block: padded scanlines + byte planes.
("noencoder /noencoder_szip_dset.h5", noencoder, 6040, 16, [168, 4, 32, 10],
"00000000010000000200000003000000040000000500000006000000070000000800000009000000"),
// <f4, LSB + NN.
("le_data /Szip_float_data_le", le_data, 55224, 48, [169, 4, 32, 12],
"abaaaa3eabaa2a3f0000803fabaa2a3f0000803fabaaaa3f0000803fabaaaa3f5555d53fabaaaa3f5555d53f00000040"),
// >f4, MSB + NN.
("le_data /Szip_float_data_be", le_data, 55396, 48, [177, 4, 32, 12],
"3eaaaaab3f2aaaab3f8000003f2aaaab3f8000003faaaaab3f8000003faaaaab3fd555553faaaaab3fd5555540000000"),
// <f8 (64-bit), NN.
("szip_h5py /f8", h5py, 4016, 100, [169, 8, 64, 10],
"00000000000008c000000000000008c000000000000008c000000000000008c000000000000004c000000000000004c000000000000004c000000000000004c000000000000000c000000000000000c000000000000000c000000000000000c0000000000000f8bf000000000000f8bf000000000000f8bf000000000000f8bf000000000000f0bf000000000000f0bf000000000000f0bf000000000000f0bf000000000000e0bf000000000000e0bf000000000000e0bf000000000000e0bf0000000000000000000000000000000000000000000000000000000000000000000000000000e03f000000000000e03f000000000000e03f000000000000e03f000000000000f03f000000000000f03f000000000000f03f000000000000f03f000000000000f83f000000000000f83f000000000000f83f000000000000f83f"),
// <i8 (64-bit), entropy coding without NN.
("szip_h5py /i8", h5py, 4188, 53, [141, 4, 64, 10],
"000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000300000000000000030000000000000003000000000000000300000000000000030000000000000003000000000000000300000000000000030000000000000006000000000000000600000000000000060000000000000006000000000000000600000000000000060000000000000006000000000000000600000000000000090000000000000009000000000000000900000000000000090000000000000009000000000000000900000000000000090000000000000009000000000000000c000000000000000c000000000000000c000000000000000c000000000000000c000000000000000c000000000000000c000000000000000c00000000000000"),
// <u2, 35 px/scanline over 8 px/block: padded scanlines, 16-bit samples.
("szip_h5py /u2", h5py, 4308, 43, [169, 8, 16, 35],
"00000000000000006100610061006100c200c200c200c20023012301230123018401840184018401e501e501e501e5014602460246024602a702a702a702a702080308030803"),
];
for (name, file, off, len, cd, want) in cases {
let want = unhex(want);
let got = szip_decompress(&file[*off..off + len], cd, want.len())
.unwrap_or_else(|e| panic!("{name}: {e:?}"));
assert_eq!(got, want, "{name}");
}
}
#[test]
fn szip_disabled_returns_unsupported() {
#[cfg(not(feature = "szip"))]
@@ -132,6 +264,8 @@ mod tests {
assert_eq!(rc, 0, "aec_buffer_encode failed: {rc}");
let enc_len = encoded.len() - enc.avail_out;
encoded.truncate(enc_len);
// H5Zszip.c prefixes the stream with the uncompressed size.
encoded.splice(0..0, (original.len() as u32).to_le_bytes());
// Decode through our public interface.
// cd[0]=0 (no NN bit 0x20), cd[1]=8 (ppb), cd[2]=8 (bpp), cd[3]=1024 (pps).
@@ -163,6 +297,8 @@ mod tests {
assert_eq!(rc, 0, "aec_buffer_encode with NN failed: {rc}");
let enc_len = encoded.len() - enc.avail_out;
encoded.truncate(enc_len);
// H5Zszip.c prefixes the stream with the uncompressed size.
encoded.splice(0..0, (original.len() as u32).to_le_bytes());
// cd[0] = 0x20 (H5_SZIP_NN_OPTION_MASK) → decoder must set AEC_DATA_PREPROCESS.
let cd = [0x20u32, 8, 8, 1024];
+28 -73
View File
@@ -6,6 +6,7 @@ extern crate alloc;
#[cfg(not(feature = "std"))]
use alloc::{format, vec, vec::Vec};
use crate::chunk_grid::ChunkGrid;
use crate::chunked_read::ChunkInfo;
use crate::error::FormatError;
@@ -151,13 +152,13 @@ pub fn read_fixed_array_chunks(
file_data: &[u8],
header: &FixedArrayHeader,
dataset_dims: &[u64],
max_dims: Option<&[u64]>,
chunk_dimensions: &[u32],
element_size: u32,
offset_size: u8,
_length_size: u8,
) -> Result<Vec<ChunkInfo>, FormatError> {
let db_offset = header.data_block_address as usize;
let rank = chunk_dimensions.len();
// Parse data block header: FADB(4) + version(1) + client_id(1) + header_address(offset_size)
let db_header_size = 4 + 1 + 1 + offset_size as usize;
@@ -198,19 +199,10 @@ pub fn read_fixed_array_chunks(
))
};
// Compute chunk offsets based on index.
// Chunks are stored in row-major order within the dataset space.
let mut num_chunks_per_dim = Vec::with_capacity(rank);
for d_idx in 0..rank {
let ch_dim = chunk_dimensions[d_idx] as u64;
if ch_dim == 0 {
return Err(FormatError::ChunkedReadError(
"chunk dimension is zero".into(),
));
}
let ds_dim = dataset_dims[d_idx];
num_chunks_per_dim.push(ds_dim.div_ceil(ch_dim));
}
// The index is laid out over the chunk grid of the *maximum* dimensions
// (row-major), so a dataset smaller than its maxshape has gaps.
let dims_u64: Vec<u64> = chunk_dimensions.iter().map(|&d| d as u64).collect();
let grid = ChunkGrid::fixed_array(dataset_dims, max_dims, &dims_u64)?;
let chunk_byte_size: u64 =
chunk_dimensions.iter().map(|&d| d as u64).product::<u64>() * element_size as u64;
@@ -226,7 +218,11 @@ pub fn read_fixed_array_chunks(
header.element_size,
chunk_byte_size,
)? {
let offsets = index_to_chunk_offsets(i, &num_chunks_per_dim, chunk_dimensions);
// A slot beyond the current extent is ignored, as the
// library does.
let Some(offsets) = grid.offsets(i as u64) else {
return Ok(());
};
chunks.push(ChunkInfo {
chunk_size,
filter_mask,
@@ -367,27 +363,6 @@ fn parse_fa_element(
}
}
/// Convert a linear chunk index to N-dimensional chunk offsets in dataset space.
fn index_to_chunk_offsets(
index: usize,
num_chunks_per_dim: &[u64],
chunk_dimensions: &[u32],
) -> Vec<u64> {
let rank = num_chunks_per_dim.len();
let mut offsets = vec![0u64; rank];
let mut remaining = index as u64;
for d in (0..rank).rev() {
let nchunks = num_chunks_per_dim[d];
if nchunks == 0 {
continue;
}
let chunk_idx = remaining % nchunks;
remaining /= nchunks;
offsets[d] = chunk_idx * chunk_dimensions[d] as u64;
}
offsets
}
/// Read a variable-length little-endian unsigned integer.
fn read_variable_length(data: &[u8], size: usize) -> Result<u64, FormatError> {
if size > 8 || data.len() < size {
@@ -416,44 +391,21 @@ mod tests {
#[test]
fn index_to_offsets_1d() {
let num_chunks = vec![5u64];
let chunk_dims = vec![20u32];
assert_eq!(index_to_chunk_offsets(0, &num_chunks, &chunk_dims), vec![0]);
assert_eq!(
index_to_chunk_offsets(1, &num_chunks, &chunk_dims),
vec![20]
);
assert_eq!(
index_to_chunk_offsets(4, &num_chunks, &chunk_dims),
vec![80]
);
let g = ChunkGrid::fixed_array(&[100], None, &[20]).unwrap();
assert_eq!(g.offsets(0).unwrap(), vec![0]);
assert_eq!(g.offsets(1).unwrap(), vec![20]);
assert_eq!(g.offsets(4).unwrap(), vec![80]);
}
#[test]
fn index_to_offsets_2d() {
// 10x6 dataset with 4x3 chunks => ceil(10/4)=3, ceil(6/3)=2 => 6 chunks
let num_chunks = vec![3u64, 2];
let chunk_dims = vec![4u32, 3];
assert_eq!(
index_to_chunk_offsets(0, &num_chunks, &chunk_dims),
vec![0, 0]
);
assert_eq!(
index_to_chunk_offsets(1, &num_chunks, &chunk_dims),
vec![0, 3]
);
assert_eq!(
index_to_chunk_offsets(2, &num_chunks, &chunk_dims),
vec![4, 0]
);
assert_eq!(
index_to_chunk_offsets(3, &num_chunks, &chunk_dims),
vec![4, 3]
);
assert_eq!(
index_to_chunk_offsets(5, &num_chunks, &chunk_dims),
vec![8, 3]
);
let g = ChunkGrid::fixed_array(&[10, 6], None, &[4, 3]).unwrap();
assert_eq!(g.offsets(0).unwrap(), vec![0, 0]);
assert_eq!(g.offsets(1).unwrap(), vec![0, 3]);
assert_eq!(g.offsets(2).unwrap(), vec![4, 0]);
assert_eq!(g.offsets(3).unwrap(), vec![4, 3]);
assert_eq!(g.offsets(5).unwrap(), vec![8, 3]);
}
#[test]
@@ -517,7 +469,7 @@ mod tests {
let read = |f: &[u8], fahd: usize| -> Result<Vec<ChunkInfo>, FormatError> {
let h = FixedArrayHeader::parse(f, fahd, 8, 8)?;
read_fixed_array_chunks(f, &h, &[60], &[20], 8, 8, 8)
read_fixed_array_chunks(f, &h, &[60], None, &[20], 8, 8, 8)
};
let (clean, fahd) = build();
@@ -562,7 +514,7 @@ mod tests {
let db = 0x100usize;
buf[db..db + 4].copy_from_slice(b"FADB");
let header = FixedArrayHeader::parse(&buf, fahd, 8, 8).unwrap();
let r = read_fixed_array_chunks(&buf, &header, &[100], &[20], 8, 8, 8);
let r = read_fixed_array_chunks(&buf, &header, &[100], None, &[20], 8, 8, 8);
assert!(r.is_err());
}
@@ -579,7 +531,7 @@ mod tests {
stamp_checksum(&mut buf, fahd, fahd + 24);
buf[0x80..0x84].copy_from_slice(b"FADB");
let header = FixedArrayHeader::parse(&buf, fahd, 8, 8).unwrap();
let r = read_fixed_array_chunks(&buf, &header, &[100], &[20], 8, 8, 8);
let r = read_fixed_array_chunks(&buf, &header, &[100], None, &[20], 8, 8, 8);
assert!(r.is_err());
}
@@ -602,7 +554,7 @@ mod tests {
data_block_address: (usize::MAX - 4) as u64,
};
let buf = vec![0u8; 64];
let r = read_fixed_array_chunks(&buf, &header, &[100], &[20], 8, 8, 8);
let r = read_fixed_array_chunks(&buf, &header, &[100], None, &[20], 8, 8, 8);
assert!(r.is_err());
}
@@ -664,6 +616,7 @@ mod tests {
&file_data,
&header,
&ds_dims,
None,
&chunk_dims,
8,
offset_size,
@@ -740,6 +693,7 @@ mod tests {
&file_data,
&header,
&ds_dims,
None,
&chunk_dims,
8,
offset_size,
@@ -840,6 +794,7 @@ mod tests {
&file_data,
&header,
&ds_dims,
None,
&chunk_dims,
8,
offset_size,
+155
View File
@@ -0,0 +1,155 @@
//! IEEE-754 half precision (binary16) conversions.
//!
//! Pure integer bit manipulation, so it works under `no_std` and needs no
//! `libm`. The writer ([`crate::type_builders::DatasetBuilder::with_f16_data`]),
//! the reader and `clawhdf5-agent`'s half-precision embedding store all use
//! these two functions, so a value rounded in memory is bit-for-bit the value
//! that reads back from the file.
/// Largest finite half-precision value. Anything larger in magnitude rounds
/// to infinity.
pub const F16_MAX: f32 = 65504.0;
/// Convert an `f32` to the bit pattern of the nearest half-precision value,
/// rounding ties to even (the IEEE default, and what numpy and the `half`
/// crate do).
///
/// Values beyond ±[`F16_MAX`] become ±infinity, values too small for a
/// subnormal become signed zero, and NaN stays NaN (quiet, payload
/// truncated).
pub fn f32_to_f16_bits(value: f32) -> u16 {
let x = value.to_bits();
let sign = (x >> 16) & 0x8000;
let exp = x & 0x7F80_0000;
let man = x & 0x007F_FFFF;
// Infinity and NaN.
if exp == 0x7F80_0000 {
let quiet_nan = if man == 0 { 0 } else { 0x0200 };
return (sign | 0x7C00 | quiet_nan | (man >> 13)) as u16;
}
let half_exp = ((exp >> 23) as i32) - 127 + 15;
// Too large: infinity.
if half_exp >= 0x1F {
return (sign | 0x7C00) as u16;
}
// Subnormal half, or zero.
if half_exp <= 0 {
if 14 - half_exp > 24 {
return sign as u16;
}
let man = man | 0x0080_0000; // implicit leading bit
let shift = (14 - half_exp) as u32;
let mut half_man = man >> shift;
let round_bit = 1u32 << (shift - 1);
// Round half to even: up if above half, or exactly half and odd.
if (man & round_bit) != 0 && (man & (3 * round_bit - 1)) != 0 {
half_man += 1;
}
return (sign | half_man) as u16;
}
// Normal half. A mantissa carry correctly rolls into the exponent (and
// from the largest finite value into infinity).
let half = sign | ((half_exp as u32) << 10) | (man >> 13);
let round_bit = 0x0000_1000;
if (man & round_bit) != 0 && (man & (3 * round_bit - 1)) != 0 {
(half + 1) as u16
} else {
half as u16
}
}
/// Convert the bit pattern of a half-precision value to `f32` (exact: every
/// half value is representable as an `f32`).
pub fn f16_bits_to_f32(h: u16) -> f32 {
let h = h as u32;
let sign = (h & 0x8000) << 16;
let exp = (h >> 10) & 0x1f;
let mant = h & 0x3ff;
let bits = if exp == 0 {
if mant == 0 {
sign // signed zero
} else {
// Subnormal: normalize into an f32 normal.
let mut e: i32 = -1;
let mut m = mant;
loop {
e += 1;
m <<= 1;
if m & 0x400 != 0 {
break;
}
}
let m = m & 0x3ff;
sign | (((127 - 15 - e) as u32) << 23) | (m << 13)
}
} else if exp == 0x1f {
sign | 0x7f80_0000 | (mant << 13) // inf / NaN
} else {
sign | ((exp + 127 - 15) << 23) | (mant << 13)
};
f32::from_bits(bits)
}
/// Round an `f32` to the nearest half-precision value, returned as `f32`.
pub fn round_to_f16(value: f32) -> f32 {
f16_bits_to_f32(f32_to_f16_bits(value))
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn every_half_value_round_trips() {
for bits in 0..=u16::MAX {
let v = f16_bits_to_f32(bits);
if v.is_nan() {
assert!(f16_bits_to_f32(f32_to_f16_bits(v)).is_nan(), "{bits:#06x}");
} else {
assert_eq!(f32_to_f16_bits(v), bits, "{bits:#06x} -> {v}");
}
}
}
#[test]
fn matches_the_half_crate() {
// Every 257th f32 bit pattern (~16.7M values) covers every exponent,
// the subnormal range, both signs, ties and the overflow boundary.
let mut bits: u32 = 0;
loop {
let v = f32::from_bits(bits);
let ours = f32_to_f16_bits(v);
let theirs = half::f16::from_f32(v);
if v.is_nan() {
assert!(theirs.is_nan() && f16_bits_to_f32(ours).is_nan());
} else {
assert_eq!(ours, theirs.to_bits(), "{bits:#010x} ({v:e})");
assert_eq!(f16_bits_to_f32(ours).to_bits(), theirs.to_f32().to_bits());
}
match bits.checked_add(257) {
Some(b) => bits = b,
None => break,
}
}
}
#[test]
fn rounds_ties_to_even_and_saturates_to_infinity() {
// 1 + 2^-11 is exactly halfway between 1.0 and the next half (1 + 2^-10).
assert_eq!(round_to_f16(1.0 + 2f32.powi(-11)), 1.0);
assert_eq!(
round_to_f16(1.0 + 3.0 * 2f32.powi(-11)),
1.0 + 2.0 * 2f32.powi(-10)
);
assert_eq!(round_to_f16(F16_MAX), F16_MAX);
assert_eq!(round_to_f16(65520.0), f32::INFINITY); // halfway to 2^16 rounds up
assert_eq!(round_to_f16(-1e9), f32::NEG_INFINITY);
assert_eq!(round_to_f16(1e-9).to_bits(), 0);
assert_eq!(round_to_f16(-1e-9).to_bits(), (-0.0f32).to_bits());
}
}
+400 -86
View File
@@ -1,12 +1,14 @@
//! HDF5 Fractal Heap parsing for v2 group link storage.
#[cfg(not(feature = "std"))]
use alloc::vec::Vec;
use alloc::{format, vec::Vec};
#[cfg(feature = "checksum")]
use byteorder::{ByteOrder, LittleEndian};
use crate::btree_v2::{BTreeV2Header, collect_btree_v2_records};
use crate::error::FormatError;
use crate::filter_pipeline::FilterPipeline;
/// Parsed fractal heap header (signature "FRHP").
#[derive(Debug, Clone)]
@@ -33,6 +35,23 @@ pub struct FractalHeapHeader {
pub current_rows_in_root_indirect_block: u16,
/// Total number of managed objects.
pub managed_objects_count: u64,
/// Address of the v2 B-tree indexing "huge" objects (undefined address
/// when the heap has none). Huge objects are those larger than
/// `max_managed_object_size`; they live outside the heap's blocks.
pub huge_btree_address: u64,
/// The heap's I/O filter pipeline, if it has one. It applies to managed
/// direct blocks and to huge objects.
pub filter_pipeline: Option<FilterPipeline>,
/// Stored (filtered) size of the root direct block; meaningful only when
/// the heap is filtered and its root is a direct block.
pub root_direct_block_filtered_size: u64,
/// Filter mask of the root direct block (bit *i* set = filter *i*
/// skipped); meaningful only when the heap is filtered.
pub root_direct_block_filter_mask: u32,
/// Size of addresses in the file ("Size of Offsets").
pub offset_size: u8,
/// Size of lengths in the file ("Size of Lengths").
pub length_size: u8,
}
fn read_offset(data: &[u8], pos: usize, size: u8) -> Result<u64, FormatError> {
@@ -79,6 +98,38 @@ fn is_undefined(val: u64, offset_size: u8) -> bool {
}
}
/// Little-endian unsigned integer of up to 8 bytes.
fn le_uint(bytes: &[u8]) -> u64 {
bytes
.iter()
.take(8)
.enumerate()
.fold(0u64, |acc, (i, &b)| acc | (u64::from(b) << (i * 8)))
}
fn heap_error(msg: &str) -> FormatError {
FormatError::ChunkedReadError(format!("fractal heap: {msg}"))
}
/// Heap ID type, from bits 4-5 of an ID's first byte (libhdf5's
/// `H5HF_ID_TYPE_MASK`, 0x30); bits 6-7 are the ID version, which must be 0.
const HEAP_ID_MANAGED: u8 = 0;
const HEAP_ID_HUGE: u8 = 1;
const HEAP_ID_TINY: u8 = 2;
/// The type (0 managed, 1 huge, 2 tiny) of a heap ID from its first byte,
/// refusing an ID version other than 0.
fn heap_id_type(first: u8) -> Result<u8, FormatError> {
if first >> 6 != 0 {
return Err(heap_error("unsupported heap ID version"));
}
Ok((first >> 4) & 0x03)
}
/// v2 B-tree record types indexing a heap's huge objects.
const BTREE_HUGE_INDIRECT: u8 = 1;
const BTREE_HUGE_INDIRECT_FILTERED: u8 = 2;
impl FractalHeapHeader {
/// Parse a fractal heap header at the given offset.
pub fn parse(
@@ -122,11 +173,17 @@ impl FractalHeapHeader {
]);
pos += 4;
// Skip several fixed fields: next_huge_object_id(ls), btree_huge_objects_address(os),
// free_space_managed_blocks(ls), managed_block_free_space_manager_address(os),
// next_huge_object_id (length_size)
ensure_len(file_data, pos, ls)?;
pos += ls;
// btree_huge_objects_address (offset_size)
let huge_btree_address = read_offset(file_data, pos, offset_size)?;
pos += os;
// Skip: free_space_managed_blocks(ls), managed_block_free_space_manager_address(os),
// managed_space_in_heap(ls), allocated_managed_space_in_heap(ls),
// direct_block_allocation_iterator_offset(ls)
let skip_size = 5 * ls + 2 * os;
let skip_size = 4 * ls + os;
ensure_len(file_data, pos, skip_size)?;
pos += skip_size;
@@ -134,14 +191,9 @@ impl FractalHeapHeader {
let managed_objects_count = read_offset(file_data, pos, length_size)?;
pos += ls;
// huge_objects_size (length_size)
pos += ls;
// huge_objects_count (length_size)
pos += ls;
// tiny_objects_size (length_size)
pos += ls;
// tiny_objects_count (length_size)
pos += ls;
// huge_objects_size, huge_objects_count, tiny_objects_size,
// tiny_objects_count (length_size each)
pos += 4 * ls;
// table_width (2)
ensure_len(file_data, pos, 2)?;
@@ -175,16 +227,28 @@ impl FractalHeapHeader {
ensure_len(file_data, pos, 2)?;
let current_rows_in_root_indirect_block =
u16::from_le_bytes([file_data[pos], file_data[pos + 1]]);
#[allow(unused_variables, unused_mut, unused_assignments)]
let mut pos = pos + 2;
pos += 2;
// Skip IO filter encoded info if present
// With I/O filters: root direct block's filtered size (length_size),
// its filter mask (4), then the encoded filter pipeline message.
let mut filter_pipeline = None;
let mut root_direct_block_filtered_size = 0;
let mut root_direct_block_filter_mask = 0;
if io_filter_encoded_length > 0 {
// root_block_filter_info_size (length_size) + filter_mask (4)
#[allow(unused_assignments)]
{
pos += ls + 4;
}
root_direct_block_filtered_size = read_offset(file_data, pos, length_size)?;
pos += ls;
ensure_len(file_data, pos, 4)?;
root_direct_block_filter_mask = u32::from_le_bytes([
file_data[pos],
file_data[pos + 1],
file_data[pos + 2],
file_data[pos + 3],
]);
pos += 4;
let n = io_filter_encoded_length as usize;
ensure_len(file_data, pos, n)?;
filter_pipeline = Some(FilterPipeline::parse(&file_data[pos..pos + n])?);
pos += n;
}
// Validate header checksum
@@ -200,6 +264,8 @@ impl FractalHeapHeader {
});
}
}
#[cfg(not(feature = "checksum"))]
let _ = pos;
Ok(FractalHeapHeader {
heap_id_length,
@@ -213,13 +279,19 @@ impl FractalHeapHeader {
root_block_address,
current_rows_in_root_indirect_block,
managed_objects_count,
huge_btree_address,
filter_pipeline,
root_direct_block_filtered_size,
root_direct_block_filter_mask,
offset_size,
length_size,
})
}
/// Decode a managed heap ID into (offset_in_heap, object_length).
///
/// The heap ID layout for managed objects (type 0):
/// - Byte 0: bits 6-7 = type (0), bits 4-5 = version (0), bits 0-3 = reserved
/// - Byte 0: bits 6-7 = version (0), bits 4-5 = type (0), bits 0-3 = reserved
/// - Bytes 1+: offset (max_heap_size bits, LE) then length (remaining bits, LE)
pub fn decode_managed_id(&self, id_bytes: &[u8]) -> Result<(u64, u64), FormatError> {
if id_bytes.is_empty() {
@@ -229,8 +301,8 @@ impl FractalHeapHeader {
});
}
let id_type = (id_bytes[0] >> 6) & 0x03;
if id_type != 0 {
let id_type = heap_id_type(id_bytes[0])?;
if id_type != HEAP_ID_MANAGED {
return Err(FormatError::InvalidHeapIdType(id_type));
}
@@ -269,12 +341,183 @@ impl FractalHeapHeader {
Ok((heap_offset, length_val))
}
/// Read a managed object from the heap given its raw heap ID bytes.
/// Read any object from the heap given its raw heap ID bytes: managed
/// (stored in the heap's blocks), huge (stored outside them, found
/// directly from the ID or through the huge-object v2 B-tree, optionally
/// filtered) or tiny (stored in the ID itself).
///
/// Despite its name this accepts every ID type; `offset_size` must match
/// the one the header was parsed with.
pub fn read_managed_object(
&self,
file_data: &[u8],
id_bytes: &[u8],
offset_size: u8,
) -> Result<Vec<u8>, FormatError> {
let Some(&first) = id_bytes.first() else {
return Err(FormatError::UnexpectedEof {
expected: 1,
available: 0,
});
};
match heap_id_type(first)? {
HEAP_ID_MANAGED => self.read_heap_managed(file_data, id_bytes, offset_size),
HEAP_ID_HUGE => self.read_huge_object(file_data, id_bytes),
HEAP_ID_TINY => self.read_tiny_object(id_bytes),
other => Err(FormatError::InvalidHeapIdType(other)),
}
}
/// Whether a huge object's ID holds its address and length directly
/// (libhdf5 does this when they fit in the ID), rather than a key into
/// the huge-object B-tree.
fn huge_ids_direct(&self) -> bool {
let room = usize::from(self.heap_id_length).saturating_sub(1);
let os = usize::from(self.offset_size);
let ls = usize::from(self.length_size);
if self.filter_pipeline.is_some() {
room >= os + ls + 4 + ls
} else {
room >= os + ls
}
}
/// Read a huge object (heap ID type 1).
fn read_huge_object(&self, file_data: &[u8], id: &[u8]) -> Result<Vec<u8>, FormatError> {
let os = usize::from(self.offset_size);
let ls = usize::from(self.length_size);
// (address, stored length, filter mask, decoded length); the last two
// only matter for a filtered heap.
let (addr, stored_len, mask, mem_len) = if self.huge_ids_direct() {
let body = &id[1..];
let need = if self.filter_pipeline.is_some() {
os + ls + 4 + ls
} else {
os + ls
};
ensure_len(body, 0, need)?;
let addr = le_uint(&body[..os]);
let len = le_uint(&body[os..os + ls]);
if self.filter_pipeline.is_some() {
let mask = u32::from_le_bytes([
body[os + ls],
body[os + ls + 1],
body[os + ls + 2],
body[os + ls + 3],
]);
let mem = le_uint(&body[os + ls + 4..os + ls + 4 + ls]);
(addr, len, mask, mem)
} else {
(addr, len, 0, len)
}
} else {
let key_len = (usize::from(self.heap_id_length).saturating_sub(1)).min(8);
ensure_len(id, 1, key_len)?;
let key = le_uint(&id[1..1 + key_len]);
self.find_huge_record(file_data, key)?
};
let start = usize::try_from(addr).map_err(|_| heap_error("huge object address"))?;
let len = usize::try_from(stored_len).map_err(|_| heap_error("huge object length"))?;
ensure_len(file_data, start, len)?;
let stored = &file_data[start..start + len];
match &self.filter_pipeline {
None => Ok(stored.to_vec()),
Some(pipeline) => {
let mem = usize::try_from(mem_len).map_err(|_| heap_error("huge object size"))?;
let out = crate::filters::decompress_chunk_masked(stored, pipeline, mem, 1, mask)?;
if out.len() != mem {
return Err(heap_error("filtered huge object decoded to the wrong size"));
}
Ok(out)
}
}
}
/// Look up huge object `key` in the huge-object v2 B-tree, returning
/// (address, stored length, filter mask, decoded length).
fn find_huge_record(
&self,
file_data: &[u8],
key: u64,
) -> Result<(u64, u64, u32, u64), FormatError> {
if is_undefined(self.huge_btree_address, self.offset_size) {
return Err(heap_error(
"huge object ID but the heap has no huge-object index",
));
}
let hdr = BTreeV2Header::parse(
file_data,
self.huge_btree_address as usize,
self.offset_size,
self.length_size,
)?;
let os = usize::from(self.offset_size);
let ls = usize::from(self.length_size);
let filtered = self.filter_pipeline.is_some();
let (expected_type, rec_len) = if filtered {
(BTREE_HUGE_INDIRECT_FILTERED, os + ls + 4 + ls + ls)
} else {
(BTREE_HUGE_INDIRECT, os + ls + ls)
};
if hdr.tree_type != expected_type || usize::from(hdr.record_size) < rec_len {
return Err(heap_error("unexpected huge-object B-tree record type"));
}
let records =
collect_btree_v2_records(file_data, &hdr, self.offset_size, self.length_size)?;
for rec in &records {
let d = &rec.data;
if d.len() < rec_len {
continue;
}
let addr = le_uint(&d[..os]);
let len = le_uint(&d[os..os + ls]);
if filtered {
let mask = u32::from_le_bytes([
d[os + ls],
d[os + ls + 1],
d[os + ls + 2],
d[os + ls + 3],
]);
let mem = le_uint(&d[os + ls + 4..os + 2 * ls + 4]);
let id = le_uint(&d[os + 2 * ls + 4..os + 3 * ls + 4]);
if id == key {
return Ok((addr, len, mask, mem));
}
} else {
let id = le_uint(&d[os + ls..os + 2 * ls]);
if id == key {
return Ok((addr, len, 0, len));
}
}
}
Err(heap_error("huge object not found in its B-tree"))
}
/// Read a tiny object (heap ID type 2), stored in the ID itself.
fn read_tiny_object(&self, id: &[u8]) -> Result<Vec<u8>, FormatError> {
// libhdf5 uses a one-byte length (low 4 bits of byte 0) unless the ID
// is long enough to need 12 bits, which then borrow byte 1.
let extended = usize::from(self.heap_id_length).saturating_sub(1) > 17;
let (len, start) = if extended {
ensure_len(id, 0, 2)?;
(
((usize::from(id[0] & 0x0F)) << 8 | usize::from(id[1])) + 1,
2,
)
} else {
(usize::from(id[0] & 0x0F) + 1, 1)
};
ensure_len(id, start, len)?;
Ok(id[start..start + len].to_vec())
}
/// Read a managed object (heap ID type 0).
fn read_heap_managed(
&self,
file_data: &[u8],
id_bytes: &[u8],
offset_size: u8,
) -> Result<Vec<u8>, FormatError> {
let (heap_offset, obj_len) = self.decode_managed_id(id_bytes)?;
@@ -289,12 +532,15 @@ impl FractalHeapHeader {
// Root is a direct block
self.read_from_direct_block(
file_data,
self.root_block_address as usize,
self.starting_block_size,
0, // block offset in heap = 0 for root
DirectBlock {
addr: self.root_block_address as usize,
size: self.starting_block_size,
heap_offset: 0,
filtered_size: self.root_direct_block_filtered_size,
filter_mask: self.root_direct_block_filter_mask,
},
heap_offset,
obj_len as usize,
offset_size,
)
} else {
// Root is an indirect block — limit recursion to 64 levels
@@ -313,27 +559,41 @@ impl FractalHeapHeader {
/// Read an object from a direct block.
///
/// The heap offset is relative to the start of the block (including its header),
/// so we just add it to the block address minus the block's heap offset.
#[allow(clippy::too_many_arguments)]
/// The heap offset is relative to the start of the block (including its
/// header), so we just add it to the block address minus the block's heap
/// offset. A filtered heap stores each direct block (header included)
/// through its filter pipeline, so the block is decoded first.
fn read_from_direct_block(
&self,
file_data: &[u8],
block_addr: usize,
_block_size: u64,
block_heap_offset: u64,
block: DirectBlock,
target_offset: u64,
length: usize,
_offset_size: u8,
) -> Result<Vec<u8>, FormatError> {
if target_offset < block_heap_offset {
if target_offset < block.heap_offset {
return Err(FormatError::UnexpectedEof {
expected: block_heap_offset as usize,
expected: block.heap_offset as usize,
available: target_offset as usize,
});
}
let local_offset = (target_offset - block_heap_offset) as usize;
let pos = block_addr
let local_offset = (target_offset - block.heap_offset) as usize;
if let Some(pipeline) = &self.filter_pipeline {
let stored_len = usize::try_from(block.filtered_size)
.map_err(|_| heap_error("direct block size"))?;
let size = usize::try_from(block.size).map_err(|_| heap_error("direct block size"))?;
ensure_len(file_data, block.addr, stored_len)?;
let decoded = crate::filters::decompress_chunk_masked(
&file_data[block.addr..block.addr + stored_len],
pipeline,
size,
1,
block.filter_mask,
)?;
ensure_len(&decoded, local_offset, length)?;
return Ok(decoded[local_offset..local_offset + length].to_vec());
}
let pos = block
.addr
.checked_add(local_offset)
.ok_or(FormatError::UnexpectedEof {
expected: usize::MAX,
@@ -371,19 +631,13 @@ impl FractalHeapHeader {
let iblock_header = 5 + offset_size as usize + block_offset_bytes;
let mut pos = iblock_addr + iblock_header;
// Compute block sizes for each row using the doubling table
let tw = self.table_width as u64;
let nrows_usize = nrows as usize;
// Build table of (block_size, heap_offset) for each child entry
let mut current_heap_offset = iblock_heap_offset;
// Rows below max_direct_rows hold direct blocks; rows at/above hold
// child indirect blocks. (NOT the FRHP "starting rows" field.)
let start_indirect = self.max_direct_rows();
// Read child addresses for direct block rows
let max_direct_rows = nrows_usize.min(start_indirect);
for row in 0..max_direct_rows {
@@ -393,48 +647,66 @@ impl FractalHeapHeader {
let child_addr = read_offset(file_data, pos, offset_size)?;
pos += offset_size as usize;
if self.io_filter_encoded_length > 0 {
// filtered_size(length_size) + filter_mask(4)
// Skip for now - we don't handle filtered direct blocks in fractal heaps
pos += 4; // filter_mask - simplified
}
// A filtered heap stores each direct block's filtered size
// (length_size) and filter mask (4) after its address.
let (filtered_size, filter_mask) = if self.filter_pipeline.is_some() {
let size = read_offset(file_data, pos, self.length_size)?;
pos += usize::from(self.length_size);
ensure_len(file_data, pos, 4)?;
let mask = u32::from_le_bytes([
file_data[pos],
file_data[pos + 1],
file_data[pos + 2],
file_data[pos + 3],
]);
pos += 4;
(size, mask)
} else {
(0, 0)
};
if !is_undefined(child_addr, offset_size) {
let block_end = current_heap_offset + block_size;
if target_offset >= current_heap_offset && target_offset < block_end {
let block_end = current_heap_offset.saturating_add(block_size);
if !is_undefined(child_addr, offset_size)
&& target_offset >= current_heap_offset
&& target_offset < block_end
{
return self.read_from_direct_block(
file_data,
child_addr as usize,
block_size,
current_heap_offset,
DirectBlock {
addr: child_addr as usize,
size: block_size,
heap_offset: current_heap_offset,
filtered_size,
filter_mask,
},
target_offset,
length,
offset_size,
);
}
}
current_heap_offset += block_size;
current_heap_offset = block_end;
}
}
// If we have indirect block rows
// Rows at and above `start_indirect` hold child indirect blocks. A
// child in row r spans exactly that row's block size of heap space,
// so it has as many rows as a table of that total size needs.
for row in start_indirect..nrows_usize {
let _block_size = self.block_size_for_row(row);
let child_nrows = row - start_indirect + 1;
let child_space = self.block_size_for_row(row);
let child_nrows = self.rows_for_size(child_space);
for _col in 0..tw {
let child_addr = read_offset(file_data, pos, offset_size)?;
pos += offset_size as usize;
if !is_undefined(child_addr, offset_size) {
// Calculate total heap space covered by this indirect block child
let total_child_space = self.indirect_block_heap_size(child_nrows);
let block_end = current_heap_offset + total_child_space;
if target_offset >= current_heap_offset && target_offset < block_end {
let block_end = current_heap_offset.saturating_add(child_space);
if !is_undefined(child_addr, offset_size)
&& target_offset >= current_heap_offset
&& target_offset < block_end
{
return self.read_from_indirect_block(
file_data,
child_addr as usize,
child_nrows as u16,
child_nrows,
current_heap_offset,
target_offset,
length,
@@ -442,11 +714,7 @@ impl FractalHeapHeader {
depth_remaining - 1,
);
}
current_heap_offset += total_child_space;
} else {
let total_child_space = self.indirect_block_heap_size(child_nrows);
current_heap_offset += total_child_space;
}
current_heap_offset = block_end;
}
}
@@ -475,25 +743,34 @@ impl FractalHeapHeader {
log2 + 2
}
/// Rows an indirect block needs to span `size` bytes of heap space:
/// `log2(size) - log2(starting_block_size * table_width) + 1`, as
/// libhdf5's `H5HF__dtable_size_to_rows`.
fn rows_for_size(&self, size: u64) -> u16 {
let log2 = |v: u64| 63u32.saturating_sub(v.max(1).leading_zeros());
let first_row_bits = log2(self.starting_block_size) + log2(u64::from(self.table_width));
(log2(size).saturating_sub(first_row_bits) + 1) as u16
}
/// Get block size for a given row in the doubling table.
fn block_size_for_row(&self, row: usize) -> u64 {
let sbs = self.starting_block_size;
if row <= 1 {
sbs
} else {
sbs * (1u64 << (row - 1))
sbs.saturating_mul(1u64.checked_shl((row - 1) as u32).unwrap_or(u64::MAX))
}
}
}
/// Total heap space covered by an indirect block with the given number of rows.
fn indirect_block_heap_size(&self, nrows: usize) -> u64 {
let tw = self.table_width as u64;
let mut total = 0u64;
for row in 0..nrows {
total += self.block_size_for_row(row) * tw;
}
total
}
/// A managed direct block's location, extent and (for a filtered heap) its
/// stored size and filter mask.
struct DirectBlock {
addr: usize,
size: u64,
heap_offset: u64,
filtered_size: u64,
filter_mask: u32,
}
#[cfg(test)]
@@ -641,7 +918,7 @@ mod tests {
let hdr = FractalHeapHeader::parse(&file_data, 0, 8, 8).unwrap();
// Build a managed heap ID:
// byte 0: type=0 (bits 6-7 = 00), version=0 (bits 4-5), reserved (bits 0-3)
// byte 0: version=0 (bits 6-7), type=0 (bits 4-5), reserved (bits 0-3)
// bytes 1-6: offset (max_heap_size=16 bits) then length (remaining bits)
// For offset=0, length=13:
// payload = offset | (length << 16) = 0 | (13 << 16) = 0x000D0000
@@ -705,9 +982,46 @@ mod tests {
fn invalid_heap_id_type() {
let (file_data, _) = build_simple_heap(8, 8);
let hdr = FractalHeapHeader::parse(&file_data, 0, 8, 8).unwrap();
// Type = 1 (tiny) in bits 6-7
let id = vec![0x40u8, 0, 0, 0, 0, 0, 0]; // bit 6 set = type 1
// Type = 1 (huge) in bits 4-5 is not a managed ID
let id = vec![0x10u8, 0, 0, 0, 0, 0, 0];
let err = hdr.decode_managed_id(&id).unwrap_err();
assert_eq!(err, FormatError::InvalidHeapIdType(1));
}
#[test]
fn tiny_object_is_read_from_the_id() {
let (file_data, _) = build_simple_heap(8, 8);
let hdr = FractalHeapHeader::parse(&file_data, 0, 8, 8).unwrap();
// Type 2 (0x20), length - 1 in the low 4 bits, data after.
let id = [0x20 | 2, b'a', b'b', b'c', 0, 0, 0];
assert_eq!(hdr.read_managed_object(&file_data, &id, 8).unwrap(), b"abc");
// A length running past the ID is an error, not a short read.
let id = [0x20 | 9, b'a', b'b', b'c', 0, 0, 0];
assert!(hdr.read_managed_object(&file_data, &id, 8).is_err());
}
#[test]
fn huge_object_with_a_direct_id() {
// With IDs long enough for an address and a length, libhdf5 stores
// huge objects' location in the ID instead of the huge-object B-tree.
let (mut file_data, _) = build_simple_heap(8, 8);
let mut hdr = FractalHeapHeader::parse(&file_data, 0, 8, 8).unwrap();
hdr.heap_id_length = 17;
file_data[900..905].copy_from_slice(b"huge!");
let mut id = vec![0x10u8];
id.extend_from_slice(&900u64.to_le_bytes());
id.extend_from_slice(&5u64.to_le_bytes());
assert_eq!(
hdr.read_managed_object(&file_data, &id, 8).unwrap(),
b"huge!"
);
}
#[test]
fn unknown_heap_id_version_is_refused() {
let (file_data, _) = build_simple_heap(8, 8);
let hdr = FractalHeapHeader::parse(&file_data, 0, 8, 8).unwrap();
let id = [0x40u8, 0, 0, 0, 0, 0, 0];
assert!(hdr.read_managed_object(&file_data, &id, 8).is_err());
}
}
+72 -5
View File
@@ -45,9 +45,16 @@ pub fn resolve_v1_group_entries(
)?;
let mut entries = Vec::new();
let mut heap_checked = false;
for snod_addr in snod_addrs {
let snod = SymbolTableNode::parse(file_data, snod_addr as usize, offset_size)?;
for entry in &snod.entries {
// Like libhdf5, look at the heap's free list only once a name is
// needed: an empty group with a damaged heap still lists.
if !heap_checked {
heap.validate_free_list(file_data, length_size)?;
heap_checked = true;
}
let name = heap.read_string(file_data, entry.link_name_offset)?;
entries.push(GroupEntry {
name,
@@ -73,6 +80,53 @@ pub fn find_v1_soft_link(
offset_size: u8,
length_size: u8,
) -> Result<Option<String>, FormatError> {
let mut found = None;
for_each_v1_soft_link(
file_data,
sym_table_msg,
offset_size,
length_size,
|link_name| link_name == name,
|_, target| {
found = Some(target);
false
},
)?;
Ok(found)
}
/// Every soft link in a v1 group, as `(name, target path)`.
pub fn v1_soft_links(
file_data: &[u8],
sym_table_msg: &SymbolTableMessage,
offset_size: u8,
length_size: u8,
) -> Result<Vec<(String, String)>, FormatError> {
let mut links = Vec::new();
for_each_v1_soft_link(
file_data,
sym_table_msg,
offset_size,
length_size,
|_| true,
|name, target| {
links.push((String::from(name), target));
true
},
)?;
Ok(links)
}
/// Visit the soft links of a v1 group whose name passes `wanted`, with their
/// target paths, until `visit` returns false.
fn for_each_v1_soft_link(
file_data: &[u8],
sym_table_msg: &SymbolTableMessage,
offset_size: u8,
length_size: u8,
wanted: impl Fn(&str) -> bool,
mut visit: impl FnMut(&str, String) -> bool,
) -> Result<(), FormatError> {
let heap = LocalHeap::parse(
file_data,
sym_table_msg.local_heap_address as usize,
@@ -85,13 +139,19 @@ pub fn find_v1_soft_link(
offset_size,
length_size,
)?;
let mut heap_checked = false;
for snod_addr in snod_addrs {
let snod = SymbolTableNode::parse(file_data, snod_addr as usize, offset_size)?;
for entry in &snod.entries {
if entry.cache_type != CACHE_TYPE_SOFT_LINK {
continue;
}
if heap.read_string(file_data, entry.link_name_offset)? != name {
if !heap_checked {
heap.validate_free_list(file_data, length_size)?;
heap_checked = true;
}
let name = heap.read_string(file_data, entry.link_name_offset)?;
if !wanted(&name) {
continue;
}
let value_offset = u32::from_le_bytes([
@@ -100,12 +160,19 @@ pub fn find_v1_soft_link(
entry.scratch_pad[2],
entry.scratch_pad[3],
]);
return heap
.read_string(file_data, u64::from(value_offset))
.map(Some);
let target = heap.read_string(file_data, u64::from(value_offset))?;
if !visit(&name, target) {
return Ok(());
}
}
Ok(None)
}
Ok(())
}
/// Whether a v1 symbol-table entry is a soft link (no object header of its
/// own; its target path is in the local heap).
pub fn is_v1_soft_link(entry: &GroupEntry) -> bool {
entry.cache_type == CACHE_TYPE_SOFT_LINK
}
/// Extract the SymbolTableMessage from an object header's messages.
+145 -21
View File
@@ -38,6 +38,24 @@ pub fn resolve_v2_group_entries(
}
}
/// First user-defined link type (HDF5 reserves 2-63; 64 is external).
const FIRST_USER_DEFINED_LINK_TYPE: u8 = 65;
/// Parse a Link message, or `None` for a user-defined link (type 65-255).
///
/// A user-defined link's target is only meaningful to the application that
/// registered its class, so, like libhdf5 without that class, we cannot
/// follow it. Leaving it out lets the rest of the group be listed and
/// resolved instead of one such link failing the whole group; reserved
/// types (2-63) are still an error.
fn parse_link(data: &[u8], offset_size: u8) -> Result<Option<LinkMessage>, FormatError> {
match LinkMessage::parse(data, offset_size) {
Ok(link) => Ok(Some(link)),
Err(FormatError::InvalidLinkType(t)) if t >= FIRST_USER_DEFINED_LINK_TYPE => Ok(None),
Err(e) => Err(e),
}
}
/// Extract link entries from Link messages directly in the object header (compact storage).
fn resolve_compact_entries(
object_header: &ObjectHeader,
@@ -46,7 +64,9 @@ fn resolve_compact_entries(
let mut entries = Vec::new();
for msg in &object_header.messages {
if msg.msg_type == MessageType::Link {
let link = LinkMessage::parse(&msg.data, offset_size)?;
let Some(link) = parse_link(&msg.data, offset_size)? else {
continue;
};
if let LinkTarget::Hard {
object_header_address,
} = link.link_target
@@ -98,7 +118,9 @@ fn for_each_dense_link(
// Read managed object from fractal heap
let link_data = fh.read_managed_object(file_data, id_bytes, offset_size)?;
visit(LinkMessage::parse(&link_data, offset_size)?);
if let Some(link) = parse_link(&link_data, offset_size)? {
visit(link);
}
}
Ok(())
}
@@ -178,7 +200,9 @@ fn find_symbolic_link(
} else {
for msg in &object_header.messages {
if msg.msg_type == MessageType::Link {
let link = LinkMessage::parse(&msg.data, offset_size)?;
let Some(link) = parse_link(&msg.data, offset_size)? else {
continue;
};
if link.name == name && is_symbolic(&link.link_target) {
found = Some(link.link_target);
}
@@ -231,32 +255,135 @@ pub fn resolve_path_any(
superblock: &Superblock,
path: &str,
) -> Result<u64, FormatError> {
resolve_path_following_links(file_data, superblock, path, 0)
resolve_path_following_links(
file_data,
superblock,
superblock.root_group_address,
path,
0,
)
}
/// Resolve `path` relative to the group at `group_address` (an absolute path
/// starts at the root group instead), following soft links. This is how a
/// relative soft link's target is resolved: from the group holding the link.
pub fn resolve_path_from(
file_data: &[u8],
superblock: &Superblock,
group_address: u64,
path: &str,
) -> Result<u64, FormatError> {
let start = if path.starts_with('/') {
superblock.root_group_address
} else {
group_address
};
resolve_path_following_links(file_data, superblock, start, path, 0)
}
/// The children of the group at `group_address` that can be opened, as h5py
/// lists them: hard links, and soft links resolved to the object they point
/// at (under the soft link's own name). Links that cannot be followed are
/// left out rather than failing the listing — a dangling or cyclic soft link
/// (h5py lists its name but cannot open it), an external link (another
/// file), and a user-defined link. An object header that is not a group has
/// no children.
///
/// Any other error, such as a corrupt structure met while resolving a soft
/// link, is returned.
pub fn resolve_group_children(
file_data: &[u8],
superblock: &Superblock,
group_address: u64,
) -> Result<Vec<GroupEntry>, FormatError> {
let os = superblock.offset_size;
let ls = superblock.length_size;
let header = ObjectHeader::parse(file_data, group_address as usize, os, ls)?;
let mut entries = Vec::new();
let mut soft = Vec::new();
if is_v1_group(&header) {
let sym_msg = header
.messages
.iter()
.find(|m| m.msg_type == MessageType::SymbolTable)
.ok_or_else(|| FormatError::PathNotFound(String::from("no symbol table message")))?;
let stm = SymbolTableMessage::parse(&sym_msg.data, os)?;
let all = group_v1::resolve_v1_group_entries(file_data, &stm, os, ls)?;
if all.iter().any(group_v1::is_v1_soft_link) {
soft = group_v1::v1_soft_links(file_data, &stm, os, ls)?;
}
entries.extend(all.into_iter().filter(|e| !group_v1::is_v1_soft_link(e)));
} else if is_v2_group(&header) {
let mut visit = |link: LinkMessage| match link.link_target {
LinkTarget::Hard {
object_header_address,
} => entries.push(GroupEntry {
name: link.name,
object_header_address,
cache_type: 0,
}),
LinkTarget::Soft { target_path } => soft.push((link.name, target_path)),
LinkTarget::External { .. } => {}
};
let link_info = find_link_info(&header, os)?;
if let Some(fh_addr) = link_info.fractal_heap_address {
for_each_dense_link(file_data, &link_info, fh_addr, os, ls, visit)?;
} else {
for msg in &header.messages {
if msg.msg_type == MessageType::Link
&& let Some(link) = parse_link(&msg.data, os)?
{
visit(link);
}
}
}
}
for (name, target) in soft {
match resolve_path_from(file_data, superblock, group_address, &target) {
Ok(object_header_address) => entries.push(GroupEntry {
name,
object_header_address,
cache_type: 0,
}),
// Dangling, cyclic, or ending in another file: not openable here.
Err(
FormatError::PathNotFound(_)
| FormatError::NestingDepthExceeded
| FormatError::ExternalLinkUnsupported { .. },
) => {}
Err(e) => return Err(e),
}
}
Ok(entries)
}
/// Soft links followed while resolving one path. Guards against link cycles
/// (`a -> b -> a`), which are legal to create.
const MAX_SOFT_LINK_DEPTH: u8 = 16;
/// Walk `path` from the group at `start`, following soft links.
fn resolve_path_following_links(
file_data: &[u8],
superblock: &Superblock,
start: u64,
path: &str,
depth: u8,
) -> Result<u64, FormatError> {
let components: Vec<&str> = path.split('/').filter(|s| !s.is_empty()).collect();
let components: Vec<&str> = path
.split('/')
.filter(|s| !s.is_empty() && *s != ".")
.collect();
if components.is_empty() {
return Ok(superblock.root_group_address);
return Ok(start);
}
let os = superblock.offset_size;
let ls = superblock.length_size;
let root_header =
ObjectHeader::parse(file_data, superblock.root_group_address as usize, os, ls)?;
let mut current_addr = superblock.root_group_address;
let mut current_header = root_header;
let mut current_addr = start;
let mut current_header = ObjectHeader::parse(file_data, start as usize, os, ls)?;
for (i, component) in components.iter().enumerate() {
let entries = resolve_group_entries(file_data, &current_header, os, ls)?;
@@ -280,20 +407,17 @@ fn resolve_path_following_links(
}
// A relative target is relative to the group holding
// the link; then the rest of the original path.
let mut full = String::new();
if !target_path.starts_with('/') {
for parent in &components[..i] {
full.push('/');
full.push_str(parent);
}
}
full.push('/');
full.push_str(&target_path);
let from = if target_path.starts_with('/') {
superblock.root_group_address
} else {
current_addr
};
let mut full = target_path;
for rest in &components[i + 1..] {
full.push('/');
full.push_str(rest);
}
resolve_path_following_links(file_data, superblock, &full, depth + 1)
resolve_path_following_links(file_data, superblock, from, &full, depth + 1)
}
Some(LinkTarget::External {
filename,
+9 -5
View File
@@ -26,12 +26,13 @@
//! use clawhdf5_format::{signature, superblock, object_header, group_v2,
//! datatype, dataspace, data_layout, data_read, message_type::MessageType};
//!
//! let file_data = std::fs::read("output.h5").unwrap();
//! let sig = signature::find_signature(&file_data).unwrap();
//! let sb = superblock::Superblock::parse(&file_data, sig).unwrap();
//! let addr = group_v2::resolve_path_any(&file_data, &sb, "data").unwrap();
//! let bytes = std::fs::read("output.h5").unwrap();
//! // Addresses are relative to the superblock: skip any user block.
//! let (_user_block, file_data) = signature::split_user_block(&bytes).unwrap();
//! let sb = superblock::Superblock::parse(file_data, 0).unwrap();
//! let addr = group_v2::resolve_path_any(file_data, &sb, "data").unwrap();
//! let hdr = object_header::ObjectHeader::parse(
//! &file_data, addr as usize, sb.offset_size, sb.length_size).unwrap();
//! file_data, addr as usize, sb.offset_size, sb.length_size).unwrap();
//! ```
//!
//! # Features
@@ -54,6 +55,7 @@ pub mod btree_v1;
pub mod btree_v2;
pub mod checksum;
pub mod chunk_cache;
mod chunk_grid;
pub mod chunk_index;
pub mod chunked_read;
pub mod chunked_write;
@@ -72,6 +74,7 @@ pub mod filter_pipeline;
pub mod filters;
mod filters_szip;
pub mod fixed_array;
pub mod float16;
pub mod fractal_heap;
pub mod global_heap;
pub mod group_info;
@@ -98,6 +101,7 @@ pub mod signature;
pub mod superblock;
pub mod symbol_table;
pub mod type_builders;
pub mod vds;
pub mod vl_data;
#[cfg(feature = "provenance")]
+97 -2
View File
@@ -87,6 +87,57 @@ impl LocalHeap {
})
}
/// Walk the free list the way libhdf5 does when it loads a heap's data
/// (`H5HL__fl_deserialize`), rejecting a heap whose free list points
/// outside the data segment. libhdf5 refuses such a heap ("bad heap free
/// list"), and names read from it would be garbage.
///
/// libhdf5 only loads a heap when it needs a name from it (an empty
/// group's broken heap goes unnoticed), so call this before the first
/// [`Self::read_string`], not on parse.
///
/// The end of the list is `H5HL_FREE_NULL` (1); an all-ones value (the
/// undefined address) is accepted as "no free list" too.
pub fn validate_free_list(&self, file_data: &[u8], length_size: u8) -> Result<(), FormatError> {
const FREE_NULL: u64 = 1;
let ls = length_size as usize;
let undefined = if ls >= 8 {
u64::MAX
} else {
(1u64 << (8 * ls)) - 1
};
let size = self.data_segment_size;
let seg = self.data_segment_address;
let mut next = self.free_list_head_offset;
// Each free block holds two lengths, so a list longer than this
// revisits a block: a cycle.
let max_blocks = size / (2 * ls as u64) + 1;
let mut walked = 0u64;
while next != FREE_NULL && next != undefined {
if next >= size || walked >= max_blocks {
return Err(FormatError::InvalidLocalHeapFreeList);
}
walked += 1;
let at = seg
.checked_add(next)
.and_then(|a| usize::try_from(a).ok())
.ok_or(FormatError::InvalidLocalHeapFreeList)?;
let block_offset = next;
next = read_offset(file_data, at, length_size)?;
if next == 0 {
return Err(FormatError::InvalidLocalHeapFreeList);
}
let block_size = read_offset(file_data, at + ls, length_size)?;
if block_offset
.checked_add(block_size)
.is_none_or(|end| end > size)
{
return Err(FormatError::InvalidLocalHeapFreeList);
}
}
Ok(())
}
/// Read a null-terminated string from the heap's data segment at the given byte offset.
pub fn read_string(&self, file_data: &[u8], string_offset: u64) -> Result<String, FormatError> {
let seg_addr = self.data_segment_address as usize;
@@ -162,8 +213,8 @@ mod tests {
// data_segment_size
write_val(&mut file, pos, data_seg_size as u64, length_size);
pos += length_size as usize;
// free_list_head_offset
write_val(&mut file, pos, 0xFFFFFFFF, length_size);
// free_list_head_offset: H5HL_FREE_NULL (no free space)
write_val(&mut file, pos, 1, length_size);
pos += length_size as usize;
// data_segment_address
write_val(&mut file, pos, data_seg_offset as u64, offset_size);
@@ -243,6 +294,50 @@ mod tests {
assert_eq!(s, "test");
}
/// Heap with data segment `[a, b, c, 0-padding]` whose free list starts
/// at `head` and has one block `(next, size)` at offset 8.
fn heap_with_free_block(head: u64, next: u64, size: u64) -> Vec<u8> {
let mut file = build_heap_file(0, 100, &["abcdefg"], 8, 8);
file.resize(200, 0);
write_val(&mut file, 8, 32, 8); // data segment size
write_val(&mut file, 16, head, 8);
write_val(&mut file, 108, next, 8);
write_val(&mut file, 116, size, 8);
file
}
#[test]
fn free_list_inside_the_segment_is_accepted() {
let file = heap_with_free_block(8, 1, 24);
let heap = LocalHeap::parse(&file, 0, 8, 8).unwrap();
heap.validate_free_list(&file, 8).unwrap();
assert_eq!(heap.read_string(&file, 0).unwrap(), "abcdefg");
// An all-ones head is "no free list" too.
let file = heap_with_free_block(u64::MAX, 0, 0);
let heap = LocalHeap::parse(&file, 0, 8, 8).unwrap();
assert!(heap.validate_free_list(&file, 8).is_ok());
}
#[test]
fn bad_free_list_is_rejected_like_libhdf5() {
for (head, next, size, why) in [
(40, 1, 8, "head past the segment"),
(8, 1, 25, "block runs past the segment"),
(8, 0, 8, "next offset of zero"),
(8, 8, 8, "cycle"),
(8, 999, 8, "next past the segment"),
] {
let file = heap_with_free_block(head, next, size);
// The header itself parses; the free list is checked on use.
let heap = LocalHeap::parse(&file, 0, 8, 8).unwrap();
assert_eq!(
heap.validate_free_list(&file, 8).unwrap_err(),
FormatError::InvalidLocalHeapFreeList,
"{why}"
);
}
}
#[test]
fn invalid_version() {
let mut file = build_heap_file(0, 100, &["x"], 8, 8);
+48 -19
View File
@@ -146,12 +146,7 @@ impl ObjectHeader {
ensure_len(data, pos, msg_data_size)?;
let msg_type = MessageType::from_u16(msg_type_raw);
// Check if unknown + must-understand (bit 3 of msg_flags)
if let MessageType::Unknown(id) = msg_type
&& msg_flags & 0x08 != 0
{
return Err(FormatError::UnsupportedMessage(id));
}
check_unknown_message(msg_type, msg_flags)?;
if msg_type != MessageType::Nil {
messages.push(HeaderMessage {
@@ -229,11 +224,7 @@ impl ObjectHeader {
let msg_type = MessageType::from_u16(msg_type_raw);
if let MessageType::Unknown(id) = msg_type
&& msg_flags & 0x08 != 0
{
return Err(FormatError::UnsupportedMessage(id));
}
check_unknown_message(msg_type, msg_flags)?;
if msg_type != MessageType::Nil {
messages.push(HeaderMessage {
@@ -424,11 +415,7 @@ impl ObjectHeader {
let msg_type = MessageType::from_u16(msg_type_raw);
if let MessageType::Unknown(id) = msg_type
&& msg_flags & 0x08 != 0
{
return Err(FormatError::UnsupportedMessage(id));
}
check_unknown_message(msg_type, msg_flags)?;
let msg_data = data[pos..pos + msg_data_size].to_vec();
@@ -509,6 +496,24 @@ impl ObjectHeader {
}
}
/// Header message flag bit 7: fail if the message is unknown, always.
const MSG_FLAG_FAIL_IF_UNKNOWN_ALWAYS: u8 = 0x80;
/// Refuse an unknown message the file says no reader may skip.
///
/// The parser only ever reads, so bit 3 (fail only when opened for writing)
/// is ignored, as libhdf5 ignores it for a read-only open; bit 7 fails
/// regardless of access mode. This had the two the wrong way round, failing
/// objects libhdf5 reads and reading ones it refuses (`tbogus.h5`).
fn check_unknown_message(msg_type: MessageType, msg_flags: u8) -> Result<(), FormatError> {
match msg_type {
MessageType::Unknown(id) if msg_flags & MSG_FLAG_FAIL_IF_UNKNOWN_ALWAYS != 0 => {
Err(FormatError::UnsupportedMessage(id))
}
_ => Ok(()),
}
}
#[cfg(test)]
mod tests {
use super::*;
@@ -632,14 +637,38 @@ mod tests {
}
#[test]
fn parse_v1_unknown_must_understand_errors() {
// Bit 3 of msg_flags = must understand
let messages = [(0x00FFu16, &[0xAA][..], 0x08u8)];
fn parse_v1_unknown_fail_always_errors() {
// Bit 7 of msg_flags = fail if unknown, whatever the access mode.
let messages = [(0x00FFu16, &[0xAA][..], 0x80u8)];
let data = build_v1_header(&messages, 8, 8);
let err = ObjectHeader::parse(&data, 0, 8, 8).unwrap_err();
assert_eq!(err, FormatError::UnsupportedMessage(0x00FF));
}
#[test]
fn parse_v1_unknown_fail_on_write_is_ignored_when_reading() {
// Bit 3 = fail if unknown *and the file is opened for writing*. This
// parser only reads, so libhdf5 (read-only) opens such an object and
// so must we. Bits 4/5 (mark if unknown / was unknown) never fail.
for flags in [0x08u8, 0x10, 0x20, 0x38] {
let messages = [(0x00FFu16, &[0xAA][..], flags)];
let data = build_v1_header(&messages, 8, 8);
let hdr = ObjectHeader::parse(&data, 0, 8, 8).unwrap();
assert_eq!(hdr.messages[0].msg_type, MessageType::Unknown(0x00FF));
}
}
#[test]
fn parse_v2_unknown_message_flags() {
let data = build_v2_header(0x00, &[(0xF0, &[1, 2], 0x08)], None);
assert!(ObjectHeader::parse(&data, 0, 8, 8).is_ok());
let data = build_v2_header(0x00, &[(0xF0, &[1, 2], 0x80)], None);
assert_eq!(
ObjectHeader::parse(&data, 0, 8, 8).unwrap_err(),
FormatError::UnsupportedMessage(0xF0)
);
}
#[test]
fn parse_v2_no_timestamps_one_message() {
let data = build_v2_header(0x00, &[(0x01, &[10, 20], 0)], None);
@@ -1,11 +1,17 @@
//! Object header writer for v2 format.
#[cfg(not(feature = "std"))]
use alloc::vec::Vec;
use alloc::{format, vec::Vec};
use crate::checksum::jenkins_lookup3;
use crate::error::FormatError;
use crate::message_type::MessageType;
/// Largest message payload a v2 object header can describe: the per-message
/// size field is 2 bytes. A bigger message cannot be encoded at all — writing
/// its size truncated to 16 bits produced files libhdf5 refuses.
pub const MAX_MESSAGE_SIZE: usize = u16::MAX as usize;
/// Writer for v2 object headers with proper checksums.
pub struct ObjectHeaderWriter {
messages: Vec<(MessageType, Vec<u8>, u8)>, // (type, data, msg_flags)
@@ -30,7 +36,22 @@ impl ObjectHeaderWriter {
}
/// Serialize the complete v2 object header (OHDR + messages + checksum).
pub fn serialize(&self) -> Vec<u8> {
///
/// Fails with [`FormatError::SerializationError`] when a message is larger
/// than [`MAX_MESSAGE_SIZE`] (e.g. an attribute over ~64 KiB, which would
/// need dense attribute storage), rather than writing a corrupt header.
pub fn serialize(&self) -> Result<Vec<u8>, FormatError> {
if let Some((msg_type, data, _)) = self
.messages
.iter()
.find(|(_, data, _)| data.len() > MAX_MESSAGE_SIZE)
{
return Err(FormatError::SerializationError(format!(
"{msg_type:?} message is {} bytes; an object header message holds at most \
{MAX_MESSAGE_SIZE} bytes",
data.len()
)));
}
// Calculate total message bytes: each message has type(1) + size(2) + flags(1) + data
let msg_bytes_total: usize = self
.messages
@@ -80,7 +101,7 @@ impl ObjectHeaderWriter {
let checksum = jenkins_lookup3(&buf);
buf.extend_from_slice(&checksum.to_le_bytes());
buf
Ok(buf)
}
}
@@ -125,15 +146,22 @@ impl BatchObjectHeaderWriter {
/// Compute the serialized size of each header without actually serializing.
/// Returns sizes in the same order as headers were added.
pub fn compute_sizes(&self) -> Vec<usize> {
self.headers.iter().map(|h| h.serialize().len()).collect()
pub fn compute_sizes(&self) -> Result<Vec<usize>, FormatError> {
self.headers
.iter()
.map(|h| h.serialize().map(|b| b.len()))
.collect()
}
/// Serialize all headers into a single contiguous buffer.
/// Returns `(combined_bytes, offsets)` where `offsets[i]` is the byte
/// offset of header `i` within the combined buffer.
pub fn serialize_all(&self) -> (Vec<u8>, Vec<usize>) {
let serialized: Vec<Vec<u8>> = self.headers.iter().map(|h| h.serialize()).collect();
pub fn serialize_all(&self) -> Result<(Vec<u8>, Vec<usize>), FormatError> {
let serialized: Vec<Vec<u8>> = self
.headers
.iter()
.map(|h| h.serialize())
.collect::<Result<_, _>>()?;
let total: usize = serialized.iter().map(|s| s.len()).sum();
let mut buf = Vec::with_capacity(total);
let mut offsets = Vec::with_capacity(serialized.len());
@@ -141,7 +169,7 @@ impl BatchObjectHeaderWriter {
offsets.push(buf.len());
buf.extend_from_slice(s);
}
(buf, offsets)
Ok((buf, offsets))
}
}
@@ -159,7 +187,7 @@ mod tests {
#[test]
fn empty_header_roundtrip() {
let writer = ObjectHeaderWriter::new();
let bytes = writer.serialize();
let bytes = writer.serialize().unwrap();
let hdr = ObjectHeader::parse(&bytes, 0, 8, 8).unwrap();
assert_eq!(hdr.version, 2);
assert_eq!(hdr.messages.len(), 0);
@@ -170,7 +198,7 @@ mod tests {
let mut writer = ObjectHeaderWriter::new();
writer.add_message(MessageType::Dataspace, vec![1, 2, 3, 4]);
writer.add_message(MessageType::Datatype, vec![5, 6]);
let bytes = writer.serialize();
let bytes = writer.serialize().unwrap();
let hdr = ObjectHeader::parse(&bytes, 0, 8, 8).unwrap();
assert_eq!(hdr.messages.len(), 2);
assert_eq!(hdr.messages[0].msg_type, MessageType::Dataspace);
@@ -184,12 +212,30 @@ mod tests {
let mut writer = ObjectHeaderWriter::new();
// Add a message with >255 bytes of payload
writer.add_message(MessageType::Datatype, vec![0xAA; 300]);
let bytes = writer.serialize();
let bytes = writer.serialize().unwrap();
let hdr = ObjectHeader::parse(&bytes, 0, 8, 8).unwrap();
assert_eq!(hdr.messages.len(), 1);
assert_eq!(hdr.messages[0].data.len(), 300);
}
#[test]
fn oversized_message_is_an_error_not_a_truncated_size() {
// 65535 bytes is the largest encodable payload.
let mut writer = ObjectHeaderWriter::new();
writer.add_message(MessageType::Attribute, vec![0; MAX_MESSAGE_SIZE]);
let bytes = writer.serialize().unwrap();
let hdr = ObjectHeader::parse(&bytes, 0, 8, 8).unwrap();
assert_eq!(hdr.messages[0].data.len(), MAX_MESSAGE_SIZE);
// One byte more used to be written with its size wrapped to 0.
let mut writer = ObjectHeaderWriter::new();
writer.add_message(MessageType::Attribute, vec![0; MAX_MESSAGE_SIZE + 1]);
assert!(matches!(
writer.serialize(),
Err(FormatError::SerializationError(_))
));
}
#[test]
fn batch_writer_serialize_all() {
let mut batch = BatchObjectHeaderWriter::new();
@@ -204,7 +250,7 @@ mod tests {
batch.add(w2);
assert_eq!(batch.len(), 2);
let (buf, offsets) = batch.serialize_all();
let (buf, offsets) = batch.serialize_all().unwrap();
assert_eq!(offsets.len(), 2);
assert_eq!(offsets[0], 0);
@@ -222,7 +268,7 @@ mod tests {
fn batch_writer_empty() {
let batch = BatchObjectHeaderWriter::new();
assert!(batch.is_empty());
let (buf, offsets) = batch.serialize_all();
let (buf, offsets) = batch.serialize_all().unwrap();
assert!(buf.is_empty());
assert!(offsets.is_empty());
}
+22 -16
View File
@@ -10,7 +10,7 @@
use crate::chunked_read::ChunkInfo;
use crate::error::FormatError;
use crate::filter_pipeline::FilterPipeline;
use crate::filters::decompress_chunk;
use crate::filters::decompress_chunk_masked;
use crate::lane_partition::{self, LaneStats, PartitionStats};
/// Threshold: only use parallel decompression when chunk count exceeds this.
@@ -84,11 +84,13 @@ pub fn decompress_chunks_lane_partitioned(
}
let raw_chunk = &file_data[c_addr..c_addr + size];
let decompressed = if chunk_info.filter_mask == 0 {
decompress_chunk(raw_chunk, pipeline, chunk_total_bytes, element_size)?
} else {
raw_chunk.to_vec()
};
let decompressed = decompress_chunk_masked(
raw_chunk,
pipeline,
chunk_total_bytes,
element_size,
chunk_info.filter_mask,
)?;
stats.chunks_processed += 1;
stats.compressed_bytes += size as u64;
@@ -158,11 +160,13 @@ pub fn decompress_chunks_parallel(
}
let raw_chunk = &file_data[c_addr..c_addr + size];
let decompressed = if chunk_info.filter_mask == 0 {
decompress_chunk(raw_chunk, pipeline, chunk_total_bytes, element_size)?
} else {
raw_chunk.to_vec()
};
let decompressed = decompress_chunk_masked(
raw_chunk,
pipeline,
chunk_total_bytes,
element_size,
chunk_info.filter_mask,
)?;
Ok(DecompressedChunk {
index,
@@ -200,11 +204,13 @@ pub fn decompress_chunks_sequential(
let raw_chunk = &file_data[c_addr..c_addr + size];
let decompressed = if let Some(pl) = pipeline {
if chunk_info.filter_mask == 0 {
decompress_chunk(raw_chunk, pl, chunk_total_bytes, element_size)?
} else {
raw_chunk.to_vec()
}
decompress_chunk_masked(
raw_chunk,
pl,
chunk_total_bytes,
element_size,
chunk_info.filter_mask,
)?
} else {
raw_chunk.to_vec()
};
+11 -5
View File
@@ -22,7 +22,7 @@ use crate::data_read::extract_selection_from_buffer;
use crate::dataspace::Dataspace;
use crate::error::FormatError;
use crate::filter_pipeline::FilterPipeline;
use crate::filters::decompress_chunk;
use crate::filters::{all_filters_skipped, decompress_chunk_masked};
use crate::selection::Selection;
/// The smallest axis-aligned box containing every selected element, as
@@ -325,12 +325,18 @@ pub fn read_selection(
expected: at.saturating_add(chunk.chunk_size as usize),
available: file_data.len(),
})?;
// Mirrors the full-read path: a non-zero filter mask means the
// chunk was stored unfiltered.
// Mirrors the full-read path: filter-mask bit i set means
// filter i was not applied to this chunk.
let decoded;
let data: &[u8] = match pipeline {
Some(pl) if chunk.filter_mask == 0 => {
decoded = decompress_chunk(raw, pl, chunk_bytes, elem_size as u32)?;
Some(pl) if !all_filters_skipped(pl, chunk.filter_mask) => {
decoded = decompress_chunk_masked(
raw,
pl,
chunk_bytes,
elem_size as u32,
chunk.filter_mask,
)?;
&decoded
}
_ => raw,
+2 -2
View File
@@ -43,7 +43,7 @@ impl Default for DatasetCreateProps {
fletcher32: false,
lz4: false,
zstd_level: None,
fill_time: FillTime::Alloc,
fill_time: FillTime::IfSet,
compact: false,
alignment: 0,
}
@@ -335,7 +335,7 @@ mod tests {
fn dcpl_defaults() {
let dcpl = DatasetCreateProps::new();
assert!(dcpl.chunk_dims.is_none());
assert_eq!(dcpl.fill_time, FillTime::Alloc);
assert_eq!(dcpl.fill_time, FillTime::IfSet);
assert!(!dcpl.compact);
}
+385 -93
View File
@@ -229,44 +229,47 @@ impl Selection {
/// self-describing in length, so the count lets a caller walk a packed list
/// of selections — as the Virtual Dataset global-heap block does).
///
/// Only the forms needed for VDS assembly are decoded: `ALL`, `NONE`, and
/// **regular** hyperslabs serialized at **version 3** (the encoding HDF5
/// 1.10+/2.0 emit). Point selections, irregular hyperslabs, and older
/// hyperslab versions return an error rather than mis-decoding.
/// Decodes `ALL`, `NONE`, and hyperslabs at every version libhdf5 writes
/// (1: irregular, 4-byte coordinates — the default-format encoding; 2:
/// regular, 8-byte; 3: either, variable width). A regular hyperslab maps
/// to [`Selection::Hyperslab`]; an *irregular* one (a union of blocks)
/// maps to a single-block hyperslab when it has one block, and otherwise to
/// [`Selection::Points`] listing the union in row-major order (the order
/// libhdf5 iterates it in). Unlimited counts/blocks decode as `u64::MAX`
/// (see [`SerializedSelection::decode`] for the raw form). Point
/// selections are refused: libhdf5 does not allow them in virtual datasets
/// either.
pub fn decode_serialized(data: &[u8]) -> Result<(Selection, usize), FormatError> {
if data.len() < 8 {
return Err(FormatError::UnexpectedEof {
expected: 8,
available: data.len(),
});
let (raw, len) = SerializedSelection::decode(data)?;
let sel = match raw {
SerializedSelection::All => Selection::All,
SerializedSelection::None => Selection::None,
SerializedSelection::Regular {
start,
stride,
count,
block,
} => Selection::Hyperslab {
start,
stride,
count,
block,
},
SerializedSelection::Blocks { rank, starts, ends } => {
if starts.len() == rank {
let block = starts.iter().zip(&ends).map(|(&s, &e)| e - s + 1).collect();
Selection::Hyperslab {
start: starts,
stride: vec![1; rank],
count: vec![1; rank],
block,
}
let sel_type = u32::from_le_bytes([data[0], data[1], data[2], data[3]]);
let version = u32::from_le_bytes([data[4], data[5], data[6], data[7]]);
match sel_type {
// ALL / NONE: type(4) + version(4) + reserved(4) + length(4) = 16 bytes.
3 | 0 => {
if data.len() < 16 {
return Err(FormatError::UnexpectedEof {
expected: 16,
available: data.len(),
});
}
let sel = if sel_type == 3 {
Selection::All
} else {
Selection::None
Selection::Points(blocks_union_coords(rank, &starts, &ends)?)
}
}
};
Ok((sel, 16))
}
2 => decode_hyperslab_serialized(data, version),
1 => Err(FormatError::ChunkedReadError(
"VDS point selections are not supported".into(),
)),
_ => Err(FormatError::ChunkedReadError(
"unknown dataspace selection type".into(),
)),
}
Ok((sel, len))
}
/// Enumerate the selected element indices of a **1-D** dataspace of the
@@ -314,6 +317,11 @@ impl Selection {
"VDS selection rank does not match dataspace rank".into(),
));
}
if count.iter().chain(block.iter()).any(|&v| v == UNLIMITED) {
return Err(FormatError::ChunkedReadError(
"unlimited selection must be clipped before it is enumerated".into(),
));
}
// Selected coordinates along each dimension, in order.
let mut per_dim: Vec<Vec<u64>> = Vec::with_capacity(rank);
for d in 0..rank {
@@ -400,59 +408,174 @@ impl Selection {
}
}
/// Decode an `H5S_SEL_HYPER` selection in its serialized form. Only version-3
/// **regular** hyperslabs are supported.
fn decode_hyperslab_serialized(
data: &[u8],
version: u32,
) -> Result<(Selection, usize), FormatError> {
if version != 3 {
return Err(FormatError::ChunkedReadError(
"only version-3 hyperslab selections are supported".into(),
));
/// Hyperslab count/block value meaning "unlimited" (`H5S_UNLIMITED`).
pub const UNLIMITED: u64 = u64::MAX;
/// Largest number of elements an irregular selection is expanded to when it
/// is converted to a point list by [`Selection::decode_serialized`].
const MAX_EXPANDED_POINTS: u64 = 1 << 26;
/// A selection exactly as `H5S_select_serialize` stores it, before it is
/// applied to any dataspace.
///
/// Unlike [`Selection`] this keeps an irregular hyperslab as its list of
/// blocks, and a regular hyperslab's count/block may be [`UNLIMITED`] (the
/// unlimited selections used by unlimited and "printf" virtual dataset
/// mappings).
#[derive(Debug, Clone, PartialEq)]
pub enum SerializedSelection {
/// `H5S_SEL_ALL`.
All,
/// `H5S_SEL_NONE`.
None,
/// A regular hyperslab. `count[d]` or `block[d]` may be [`UNLIMITED`].
Regular {
start: Vec<u64>,
stride: Vec<u64>,
count: Vec<u64>,
block: Vec<u64>,
},
/// An irregular hyperslab: the union of `starts.len() / rank` blocks, each
/// given by its first (`starts`) and last (`ends`, inclusive) coordinate,
/// flattened block-major.
Blocks {
rank: usize,
starts: Vec<u64>,
ends: Vec<u64>,
},
}
// type(4) ver(4) flags(1) enc_size(1) rank(4) [start,stride,count,block]*rank
if data.len() < 14 {
return Err(FormatError::UnexpectedEof {
expected: 14,
available: data.len(),
});
fn sel_err(msg: &str) -> FormatError {
FormatError::ChunkedReadError(msg.into())
}
let flags = data[8];
let enc_size = data[9] as usize;
// Bit 0 set => regular hyperslab. Irregular hyperslabs list explicit blocks.
if flags & 0x01 == 0 {
return Err(FormatError::ChunkedReadError(
"irregular VDS hyperslab selections are not supported".into(),
));
/// Bounds-checked little-endian reader over a serialized selection.
struct SelReader<'a> {
data: &'a [u8],
pos: usize,
}
if enc_size != 2 && enc_size != 4 && enc_size != 8 {
return Err(FormatError::ChunkedReadError(
"unsupported hyperslab coordinate encoding size".into(),
));
impl SelReader<'_> {
fn take(&mut self, n: usize) -> Result<&[u8], FormatError> {
let end = self.pos.checked_add(n).filter(|&e| e <= self.data.len());
let end = end.ok_or(FormatError::UnexpectedEof {
expected: self.pos.saturating_add(n),
available: self.data.len(),
})?;
let s = &self.data[self.pos..end];
self.pos = end;
Ok(s)
}
let rank = u32::from_le_bytes([data[10], data[11], data[12], data[13]]) as usize;
// HDF5 caps dataspace rank at 32 (H5S_MAX_RANK). Reject anything larger so a
// corrupt rank can't drive a huge allocation or read loop.
if rank > 32 {
return Err(FormatError::ChunkedReadError(
"hyperslab selection rank exceeds maximum (32)".into(),
));
fn uint(&mut self, size: usize) -> Result<u64, FormatError> {
let bytes = self.take(size)?;
Ok(bytes
.iter()
.enumerate()
.fold(0u64, |v, (i, &b)| v | (b as u64) << (i * 8)))
}
let mut pos = 14;
let read_coord = |data: &[u8], pos: usize| -> Result<u64, FormatError> {
if pos + enc_size > data.len() {
return Err(FormatError::UnexpectedEof {
expected: pos + enc_size,
available: data.len(),
});
fn remaining(&self) -> usize {
self.data.len() - self.pos
}
let mut v = 0u64;
for (i, &b) in data[pos..pos + enc_size].iter().enumerate() {
v |= (b as u64) << (i * 8);
}
Ok(v)
impl SerializedSelection {
/// Decode a serialized selection, returning it and the number of bytes it
/// occupies. Mirrors libhdf5's `H5S_select_deserialize`: `ALL`/`NONE` and
/// hyperslab versions 1-3 are decoded; point selections (which libhdf5
/// refuses in virtual datasets) and malformed input are errors.
pub fn decode(data: &[u8]) -> Result<(SerializedSelection, usize), FormatError> {
let mut r = SelReader { data, pos: 0 };
let sel_type = r.uint(4)?;
let version = r.uint(4)?;
match sel_type {
// ALL / NONE: type(4) + version(4) + reserved(4) + length(4).
0 | 3 => {
r.take(8)?;
let sel = if sel_type == 3 {
SerializedSelection::All
} else {
SerializedSelection::None
};
Ok((sel, r.pos))
}
2 => {
let sel = decode_hyperslab(&mut r, version)?;
Ok((sel, r.pos))
}
1 => Err(sel_err(
"VDS point selections are not supported (libhdf5 rejects them too)",
)),
_ => Err(sel_err("unknown dataspace selection type")),
}
}
/// The single dimension in which this selection is unlimited, if any.
pub fn unlimited_dim(&self) -> Option<usize> {
match self {
SerializedSelection::Regular { count, block, .. } => count
.iter()
.zip(block)
.position(|(&c, &b)| c == UNLIMITED || b == UNLIMITED),
_ => None,
}
}
/// The rank the selection was serialized with (`None` for ALL/NONE, which
/// carry no rank).
pub fn rank(&self) -> Option<usize> {
match self {
SerializedSelection::Regular { start, .. } => Some(start.len()),
SerializedSelection::Blocks { rank, .. } => Some(*rank),
_ => None,
}
}
}
/// `H5S__hyper_deserialize`: after the type and version words.
fn decode_hyperslab(r: &mut SelReader, version: u64) -> Result<SerializedSelection, FormatError> {
const REGULAR: u8 = 0x01;
let (flags, enc_size) = match version {
// v1: reserved(4) + length(4), always irregular, 4-byte coordinates.
1 => {
r.take(8)?;
(0u8, 4usize)
}
// v2: flags(1) + length(4), 8-byte coordinates.
2 => {
let flags = r.take(1)?[0];
r.take(4)?;
(flags, 8)
}
// v3: flags(1) + encoding size(1).
3 => {
let flags = r.take(1)?[0];
let enc = r.take(1)?[0] as usize;
(flags, enc)
}
_ => return Err(sel_err("unsupported hyperslab selection version")),
};
if flags & !REGULAR != 0 {
return Err(sel_err("unknown hyperslab selection flags"));
}
if !matches!(enc_size, 2 | 4 | 8) {
return Err(sel_err("unsupported hyperslab coordinate encoding size"));
}
let rank = r.uint(4)? as usize;
// HDF5 caps dataspace rank at 32 (H5S_MAX_RANK). Reject anything else so a
// corrupt rank can't drive a huge allocation or read loop.
if rank == 0 || rank > 32 {
return Err(sel_err("hyperslab selection rank must be 1..=32"));
}
// The all-ones value of the encoding width means "unlimited".
let unlim_raw = if enc_size == 8 {
u64::MAX
} else {
(1u64 << (enc_size * 8)) - 1
};
if flags & REGULAR != 0 {
let (mut start, mut stride, mut count, mut block) = (
Vec::with_capacity(rank),
Vec::with_capacity(rank),
@@ -460,24 +583,104 @@ fn decode_hyperslab_serialized(
Vec::with_capacity(rank),
);
for _ in 0..rank {
start.push(read_coord(data, pos)?);
pos += enc_size;
stride.push(read_coord(data, pos)?);
pos += enc_size;
count.push(read_coord(data, pos)?);
pos += enc_size;
block.push(read_coord(data, pos)?);
pos += enc_size;
start.push(r.uint(enc_size)?);
stride.push(r.uint(enc_size)?);
let c = r.uint(enc_size)?;
count.push(if c == unlim_raw { UNLIMITED } else { c });
let b = r.uint(enc_size)?;
block.push(if b == unlim_raw { UNLIMITED } else { b });
}
Ok((
Selection::Hyperslab {
let unlimited = count
.iter()
.zip(&block)
.filter(|&(&c, &b)| c == UNLIMITED || b == UNLIMITED)
.count();
if unlimited > 1 {
return Err(sel_err(
"hyperslab selection is unlimited in more than one dimension",
));
}
for d in 0..rank {
// Overlapping blocks are not a valid regular hyperslab.
if count[d] > 1 && block[d] != UNLIMITED && block[d] > stride[d] {
return Err(sel_err("regular hyperslab blocks overlap"));
}
}
return Ok(SerializedSelection::Regular {
start,
stride,
count,
block,
},
pos,
))
});
}
// Irregular: number of blocks, then each block's start and end corners.
let nblocks = r.uint(enc_size)?;
let per_block = (rank * 2 * enc_size) as u64;
// Untrusted count: it must fit in what is left of the buffer.
if nblocks
.checked_mul(per_block)
.is_none_or(|need| need > r.remaining() as u64)
{
return Err(FormatError::UnexpectedEof {
expected: r
.pos
.saturating_add(nblocks.saturating_mul(per_block) as usize),
available: r.data.len(),
});
}
let n = nblocks as usize * rank;
let (mut starts, mut ends) = (Vec::with_capacity(n), Vec::with_capacity(n));
for _ in 0..nblocks {
for _ in 0..rank {
starts.push(r.uint(enc_size)?);
}
for _ in 0..rank {
ends.push(r.uint(enc_size)?);
}
}
if starts.iter().zip(&ends).any(|(s, e)| e < s) {
return Err(sel_err("hyperslab block ends before it starts"));
}
Ok(SerializedSelection::Blocks { rank, starts, ends })
}
/// The coordinates of the union of the given blocks, in row-major order.
fn blocks_union_coords(
rank: usize,
starts: &[u64],
ends: &[u64],
) -> Result<Vec<Vec<u64>>, FormatError> {
let mut total = 0u64;
for (s, e) in starts.chunks_exact(rank).zip(ends.chunks_exact(rank)) {
let vol = s
.iter()
.zip(e)
.try_fold(1u64, |acc, (&s, &e)| acc.checked_mul(e - s + 1));
total = vol
.and_then(|v| total.checked_add(v))
.filter(|&t| t <= MAX_EXPANDED_POINTS)
.ok_or_else(|| sel_err("irregular hyperslab selection is too large to expand"))?;
}
let mut out = Vec::with_capacity(total as usize);
for (s, e) in starts.chunks_exact(rank).zip(ends.chunks_exact(rank)) {
let mut cur = s.to_vec();
'block: loop {
out.push(cur.clone());
for d in (0..rank).rev() {
if cur[d] < e[d] {
cur[d] += 1;
continue 'block;
}
cur[d] = s[d];
}
break;
}
}
// Lexicographic order of coordinates is row-major order.
out.sort_unstable();
out.dedup();
Ok(out)
}
// ---------------------------------------------------------------------------
@@ -642,11 +845,100 @@ mod tests {
}
#[test]
fn decode_irregular_hyperslab_rejected() {
fn decode_truncated_irregular_hyperslab_is_error() {
// Irregular, rank 1, but the block count is missing.
let bytes = [0x02u8, 0, 0, 0, 0x03, 0, 0, 0, 0x00, 0x02, 0x01, 0, 0, 0];
assert!(Selection::decode_serialized(&bytes).is_err());
}
/// Version 1 as libhdf5 writes it for the default (earliest) format bounds:
/// type, version, reserved(4), length(4), rank(4), nblocks(4), then each
/// block's start and inclusive end corner as 4-byte values.
fn v1_blocks(rank: u32, blocks: &[(&[u32], &[u32])]) -> Vec<u8> {
let mut b = Vec::new();
for w in [2u32, 1, 0, 0, rank, blocks.len() as u32] {
b.extend_from_slice(&w.to_le_bytes());
}
for (s, e) in blocks {
for v in s.iter().chain(e.iter()) {
b.extend_from_slice(&v.to_le_bytes());
}
}
b
}
#[test]
fn decode_v1_irregular_single_block() {
// Exactly what h5py/HDF5 2.0 writes for `[0:4]` with default libver.
let bytes = v1_blocks(1, &[(&[0], &[3])]);
let (sel, used) = Selection::decode_serialized(&bytes).unwrap();
assert_eq!(used, bytes.len());
assert_eq!(sel.iter_linear_1d(8).unwrap(), vec![0, 1, 2, 3]);
}
#[test]
fn decode_v1_irregular_union_is_row_major() {
// Blocks given out of order and overlapping still enumerate once each,
// in row-major order (libhdf5 iterates the union, not the list).
let bytes = v1_blocks(2, &[(&[1, 0], &[1, 1]), (&[0, 2], &[1, 2])]);
let (sel, used) = Selection::decode_serialized(&bytes).unwrap();
assert_eq!(used, bytes.len());
// (0,2) (1,0) (1,1) (1,2) in a 2x3 space.
assert_eq!(sel.iter_linear(&[2, 3]).unwrap(), vec![2, 3, 4, 5]);
}
#[test]
fn decode_v2_regular_with_unlimited_count() {
// v2: flags(1) + length(4), then 8-byte start/stride/count/block.
let mut b = Vec::new();
b.extend_from_slice(&2u32.to_le_bytes());
b.extend_from_slice(&2u32.to_le_bytes());
b.push(0x01);
b.extend_from_slice(&36u32.to_le_bytes());
b.extend_from_slice(&1u32.to_le_bytes());
for v in [0u64, 10, u64::MAX, 10] {
b.extend_from_slice(&v.to_le_bytes());
}
let (raw, used) = SerializedSelection::decode(&b).unwrap();
assert_eq!(used, b.len());
assert_eq!(raw.unlimited_dim(), Some(0));
assert_eq!(
raw,
SerializedSelection::Regular {
start: vec![0],
stride: vec![10],
count: vec![UNLIMITED],
block: vec![10],
}
);
// An unclipped unlimited selection cannot be enumerated.
let (sel, _) = Selection::decode_serialized(&b).unwrap();
assert!(sel.iter_linear_1d(100).is_err());
}
#[test]
fn decode_v3_two_byte_all_ones_is_unlimited() {
let bytes = [
0x02, 0, 0, 0, 0x03, 0, 0, 0, 0x01, 0x02, 0x01, 0, 0, 0, //
0x00, 0x00, 0x01, 0x00, 0x01, 0x00, 0xFF, 0xFF,
];
let (raw, _) = SerializedSelection::decode(&bytes).unwrap();
assert_eq!(raw.unlimited_dim(), Some(0));
}
#[test]
fn decode_irregular_block_count_beyond_buffer_is_error() {
let mut b = v1_blocks(1, &[(&[0], &[3])]);
b[20..24].copy_from_slice(&u32::MAX.to_le_bytes());
assert!(Selection::decode_serialized(&b).is_err());
}
#[test]
fn decode_point_selection_is_refused() {
let bytes = [1u8, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
assert!(Selection::decode_serialized(&bytes).is_err());
}
#[test]
fn iter_linear_2d_block_row_major() {
// A 2x2 block at the top-left of a 4x4 space => linear 0,1,4,5.
+120 -16
View File
@@ -154,13 +154,29 @@ pub fn is_shared(msg_flags: u8) -> bool {
///
/// When the shared flag is set on a message, the data contains a reference
/// instead of the actual message content.
///
/// Assumes the file's length size equals its offset size, which only matters
/// for version-1 references; use [`parse_shared_ref_sized`] when the
/// superblock's length size is known.
pub fn parse_shared_ref(data: &[u8], offset_size: u8) -> Result<SharedMessageRef, FormatError> {
parse_shared_ref_sized(data, offset_size, offset_size)
}
/// [`parse_shared_ref`] with the superblock's length size, which locates the
/// object header address in a version-1 reference.
pub fn parse_shared_ref_sized(
data: &[u8],
offset_size: u8,
length_size: u8,
) -> Result<SharedMessageRef, FormatError> {
ensure_len(data, 0, 2)?;
let version = data[0];
let ref_type = data[1];
// Layouts (HDF5 spec IV.A.2 "Shared Message", and libhdf5's decoder):
// v1: version, type, reserved(6), address — always "committed"
// v1: version, type, reserved(6), then an old-style symbol table
// entry: link-name offset(length_size), object header address,
// cache type(4), reserved(4), scratch(16) — always "committed"
// v2: version, type, address — always "committed"
// v3: version, type, then a fractal-heap ID if type == SOHM, otherwise
// an address
@@ -177,7 +193,7 @@ pub fn parse_shared_ref(data: &[u8], offset_size: u8) -> Result<SharedMessageRef
})
};
match version {
1 => address_at(2 + 6),
1 => address_at(2 + 6 + length_size as usize),
2 => address_at(2),
3 if ref_type == SHARE_TYPE_SOHM => {
ensure_len(data, 2, FHEAP_ID_LEN)?;
@@ -225,9 +241,12 @@ pub fn parse_sohm_table_message(
/// Parse the SOHM table structure (signature "SMTB") from the file.
///
/// Each index entry: index_type(1) + mesg_types(2) + min_mesg_size(4) +
/// list_max(2) + btree_min(2) + num_messages(2) + index_addr(offset_size) +
/// heap_addr(offset_size)
/// Each index entry: version(1) + index_type(1) + mesg_types(2) +
/// min_mesg_size(4) + list_max(2) + btree_min(2) + num_messages(2) +
/// index_addr(offset_size) + heap_addr(offset_size)
///
/// The leading per-index version byte (0) was missing here, so every field
/// after it was read one byte off — verified against an HDF5 2.0 file.
pub fn parse_sohm_table(
file_data: &[u8],
table_addr: usize,
@@ -240,11 +259,16 @@ pub fn parse_sohm_table(
}
let mut pos = table_addr + 4;
let os = offset_size as usize;
let entry_size = 1 + 2 + 4 + 2 + 2 + 2 + os + os; // 13 + 2*offset_size
let entry_size = 1 + 1 + 2 + 4 + 2 + 2 + 2 + os + os; // 14 + 2*offset_size
let mut indexes = Vec::with_capacity(nindexes as usize);
for _ in 0..nindexes {
ensure_len(file_data, pos, entry_size)?;
let version = file_data[pos];
if version != 0 {
return Err(FormatError::InvalidSohmTableVersion(version));
}
pos += 1;
let index_type = file_data[pos];
pos += 1;
let mesg_types = u16::from_le_bytes([file_data[pos], file_data[pos + 1]]);
@@ -381,6 +405,68 @@ pub fn parse_sohm_btree_entries(
// ---- SOHM resolution ----
/// Find the SOHM index that handles the given message type.
/// Load a file's SOHM table: superblock → superblock extension → Shared
/// Message Table message → SMTB. `Ok(None)` when the file has no superblock
/// extension or no shared-message table.
pub fn load_sohm_table(
file_data: &[u8],
offset_size: u8,
length_size: u8,
) -> Result<Option<SohmTable>, FormatError> {
let sig = crate::signature::find_signature(file_data)?;
let sb = crate::superblock::Superblock::parse(file_data, sig)?;
let Some(ext_addr) = sb
.superblock_extension_address
.filter(|&a| !is_undefined(a, offset_size))
else {
return Ok(None);
};
let ext = ObjectHeader::parse(file_data, ext_addr as usize, offset_size, length_size)?;
let Some(msg) = ext
.messages
.iter()
.find(|m| m.msg_type == MessageType::SharedMessageTable)
else {
return Ok(None);
};
let table_msg = parse_sohm_table_message(&msg.data, offset_size)?;
parse_sohm_table(
file_data,
table_msg.table_address as usize,
table_msg.nindexes,
offset_size,
)
.map(Some)
}
/// Like [`message_data`], but also follows references into the file's SOHM
/// heap (shared object header messages), loading the SOHM table on demand.
pub fn message_data_with_sohm<'a>(
file_data: &[u8],
msg: &'a crate::object_header::HeaderMessage,
offset_size: u8,
length_size: u8,
) -> Result<Cow<'a, [u8]>, FormatError> {
if !is_shared(msg.flags) {
return Ok(Cow::Borrowed(&msg.data));
}
let shared_ref = parse_shared_ref_sized(&msg.data, offset_size, length_size)?;
let table = if shared_ref.heap_id.is_some() {
load_sohm_table(file_data, offset_size, length_size)?
} else {
None
};
resolve_shared_message_with_sohm(
file_data,
&shared_ref,
msg.msg_type,
offset_size,
length_size,
table.as_ref(),
)
.map(Cow::Owned)
}
fn find_index_for_msg_type(table: &SohmTable, msg_type: MessageType) -> Option<&SohmIndex> {
let type_bit = 1u16 << msg_type.to_u16();
table
@@ -444,7 +530,7 @@ pub fn message_data<'a>(
if !is_shared(msg.flags) {
return Ok(Cow::Borrowed(&msg.data));
}
let shared_ref = parse_shared_ref(&msg.data, offset_size)?;
let shared_ref = parse_shared_ref_sized(&msg.data, offset_size, length_size)?;
resolve_shared_message(
file_data,
&shared_ref,
@@ -459,7 +545,8 @@ pub fn message_data<'a>(
///
/// For type 1/3 (shared in another object header), reads the target object header
/// and finds the message of the specified type.
/// For type 2 (SOHM), uses the fractal heap from the SOHM table.
/// For type 2 (SOHM), uses the fractal heap from the file's SOHM table,
/// loaded from the superblock extension on demand.
pub fn resolve_shared_message(
file_data: &[u8],
shared_ref: &SharedMessageRef,
@@ -467,13 +554,18 @@ pub fn resolve_shared_message(
offset_size: u8,
length_size: u8,
) -> Result<Vec<u8>, FormatError> {
let table = if shared_ref.heap_id.is_some() {
load_sohm_table(file_data, offset_size, length_size)?
} else {
None
};
resolve_shared_message_with_sohm(
file_data,
shared_ref,
target_msg_type,
offset_size,
length_size,
None,
table.as_ref(),
)
}
@@ -579,15 +671,26 @@ mod tests {
#[test]
fn parse_v1_ref() {
let mut data = Vec::new();
data.push(1); // version
data.push(0); // type
data.extend_from_slice(&[0u8; 6]); // reserved
data.extend_from_slice(&0x5678u64.to_le_bytes());
// Datatype message of `/group1/dset2` in HDF5's `tcompound.h5`
// (written in 2000): version 1, six reserved bytes, then an old-style
// symbol table entry — link-name offset 0x10, object header address
// 0x590 (the committed datatype `/type1`), cache type, reserved and
// scratch.
let mut data = vec![1, 0, 0, 0, 0, 0, 0, 0];
data.extend_from_slice(&0x10u64.to_le_bytes());
data.extend_from_slice(&0x590u64.to_le_bytes());
data.extend_from_slice(&[0; 24]);
let shared = parse_shared_ref(&data, 8).unwrap();
let shared = parse_shared_ref_sized(&data, 8, 8).unwrap();
assert_eq!(shared.version, 1);
assert_eq!(shared.object_header_address, Some(0x5678));
assert_eq!(shared.object_header_address, Some(0x590));
// The name offset is a length: 4 bytes here, then an 8-byte address.
let mut data = vec![1, 0, 0, 0, 0, 0, 0, 0];
data.extend_from_slice(&0x10u32.to_le_bytes());
data.extend_from_slice(&0x590u64.to_le_bytes());
let shared = parse_shared_ref_sized(&data, 8, 4).unwrap();
assert_eq!(shared.object_header_address, Some(0x590));
}
#[test]
@@ -707,6 +810,7 @@ mod tests {
let mut buf = Vec::new();
buf.extend_from_slice(b"SMTB");
for idx in indexes {
buf.push(0); // version
buf.push(idx.index_type);
buf.extend_from_slice(&idx.mesg_types.to_le_bytes());
buf.extend_from_slice(&idx.min_mesg_size.to_le_bytes());
+36
View File
@@ -11,6 +11,16 @@ pub const HDF5_SIGNATURE: [u8; 8] = [0x89, b'H', b'D', b'F', b'\r', b'\n', 0x1A,
/// (powers of two starting at 512, plus offset 0).
///
/// Returns the byte offset where the signature was found.
///
/// A non-zero offset means the file starts with a *user block*, and every
/// address inside the file is relative to the superblock's position, not to
/// byte 0 (libhdf5 uses the signature's position as the base address even
/// when the stored base-address field disagrees). The parsers in this crate
/// take addresses as indices into `file_data`, so they must be handed the
/// bytes from the signature on — use [`split_user_block`]. [`Superblock::parse`]
/// refuses a non-zero offset for this reason.
///
/// [`Superblock::parse`]: crate::superblock::Superblock::parse
pub fn find_signature(data: &[u8]) -> Result<usize, FormatError> {
// Check offset 0
if data.len() >= 8 && data[..8] == HDF5_SIGNATURE {
@@ -29,6 +39,17 @@ pub fn find_signature(data: &[u8]) -> Result<usize, FormatError> {
Err(FormatError::SignatureNotFound)
}
/// Split a file into its user block and its HDF5 bytes.
///
/// Returns `(user_block, hdf5)`: `user_block` is everything before the
/// superblock signature (empty for most files) and `hdf5` is the rest, in
/// which every HDF5 address is a plain index. Pass `hdf5` as `file_data` to
/// every parser in this crate, and parse the superblock at offset 0 of it.
pub fn split_user_block(data: &[u8]) -> Result<(&[u8], &[u8]), FormatError> {
let offset = find_signature(data)?;
Ok(data.split_at(offset))
}
#[cfg(test)]
mod tests {
use super::*;
@@ -88,6 +109,21 @@ mod tests {
assert_eq!(find_signature(&data), Err(FormatError::SignatureNotFound));
}
#[test]
fn split_user_block_rebases_at_the_signature() {
let mut data = vec![7u8; 1024];
data[512..520].copy_from_slice(&HDF5_SIGNATURE);
let (ub, hdf5) = split_user_block(&data).unwrap();
assert_eq!(ub.len(), 512);
assert_eq!(hdf5.len(), 512);
assert_eq!(&hdf5[..8], &HDF5_SIGNATURE);
data[..8].copy_from_slice(&HDF5_SIGNATURE);
let (ub, hdf5) = split_user_block(&data).unwrap();
assert!(ub.is_empty());
assert_eq!(hdf5.len(), 1024);
}
#[test]
fn signature_prefers_earliest() {
// Signature at both 0 and 512, should return 0
+31 -5
View File
@@ -39,7 +39,13 @@ pub struct Superblock {
pub superblock_extension_address: Option<u64>,
/// CRC32C checksum (v2/v3 only).
pub checksum: Option<u32>,
/// Page size for page-buffer mode (v4 only). `None` for v0–v3.
/// Page size of the non-standard "version 4" superblock layout (v4 only).
/// `None` for v0–v3.
///
/// HDF5 has no superblock version 4 — libhdf5 refuses it. A real paged
/// file is a v2/v3 superblock whose extension holds a File Space Info
/// message (what `FileWriter::with_page_size` writes). This field is kept
/// only so such files written by older clawhdf5 versions still parse.
pub page_size: Option<u32>,
}
@@ -127,8 +133,9 @@ impl Superblock {
/// Serialize this superblock to bytes.
///
/// Writes v2/v3 format, or v4 (with `page_size`) when `self.version == 4`.
/// Computes and appends Jenkins lookup3 checksum.
/// Writes v2/v3 format, or the non-standard v4 (with `page_size`) when
/// `self.version == 4` — which no HDF5 library opens; see
/// [`Self::page_size`]. Computes and appends Jenkins lookup3 checksum.
pub fn serialize(&self) -> Vec<u8> {
let mut buf = Vec::with_capacity(48);
buf.extend_from_slice(&HDF5_SIGNATURE);
@@ -167,8 +174,18 @@ impl Superblock {
/// Parse a superblock from `data` starting at `signature_offset`.
///
/// The signature must be present at the given offset.
/// The signature must be present at the given offset, and that offset
/// must be 0: every address in an HDF5 file is relative to the
/// superblock, so when a file has a user block (signature at 512, 1024,
/// …) the caller must pass the bytes from the signature on — see
/// [`crate::signature::split_user_block`] — and use that slice as
/// `file_data` everywhere. A non-zero offset is refused with
/// [`FormatError::UserBlockNotStripped`] because the addresses in the
/// returned superblock would otherwise be applied to the wrong bytes.
pub fn parse(data: &[u8], signature_offset: usize) -> Result<Superblock, FormatError> {
if signature_offset != 0 {
return Err(FormatError::UserBlockNotStripped(signature_offset as u64));
}
let d = data
.get(signature_offset..)
.ok_or(FormatError::UnexpectedEof {
@@ -669,7 +686,16 @@ mod tests {
let mut data = vec![0u8; 1024];
let v0 = build_v0_bytes(8);
data[512..512 + v0.len()].copy_from_slice(&v0);
let sb = Superblock::parse(&data, 512).unwrap();
// Addresses are relative to the superblock, so parsing in place
// (where they would be applied to the whole buffer) is refused...
assert_eq!(
Superblock::parse(&data, 512),
Err(FormatError::UserBlockNotStripped(512))
);
// ...and the caller parses the bytes from the signature on.
let (ub, hdf5) = crate::signature::split_user_block(&data).unwrap();
assert_eq!(ub.len(), 512);
let sb = Superblock::parse(hdf5, 0).unwrap();
assert_eq!(sb.version, 0);
assert_eq!(sb.root_group_address, 96);
}
+125 -17
View File
@@ -15,31 +15,83 @@ use crate::datatype::{
/// Controls when fill values are written to dataset storage.
///
/// Corresponds to the HDF5 fill value message's "fill time" field.
/// Corresponds to the HDF5 fill value message's "fill time" field
/// (`H5D_fill_time_t`).
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
pub enum FillTime {
/// Never write fill values (0x02). Avoids initialization overhead
/// for datasets that will be fully written before any read.
/// Never write fill values (`H5D_FILL_TIME_NEVER`). Avoids
/// initialization overhead for datasets that will be fully written
/// before any read.
Never,
/// Write fill values at allocation time (0x0a). This is the default
/// and matches the HDF5 C library's behavior.
#[default]
/// Write fill values when storage is allocated (`H5D_FILL_TIME_ALLOC`).
Alloc,
/// Write fill values only when the fill value has been explicitly set (0x06).
/// Write fill values at allocation only if one was set explicitly
/// (`H5D_FILL_TIME_IFSET`). The default, as in the HDF5 C library.
#[default]
IfSet,
}
/// Space allocation time written with every fill value message: late
/// (`H5D_ALLOC_TIME_LATE`), bits 0-1 of the flags byte.
const ALLOC_TIME_LATE: u8 = 2;
impl FillTime {
/// Serialize to the byte used in the fill value message (version 3).
/// Serialize to the flags byte of a version 3 fill value message: the
/// space allocation time (late) in bits 0-1 and the fill time in bits
/// 2-3 (`H5D_FILL_TIME_ALLOC` = 0, `NEVER` = 1, `IFSET` = 2).
///
/// This used to put `Never` in the ALLOC slot, `Alloc` in IFSET and
/// `IfSet` in NEVER, so libhdf5 saw every choice as a different one.
pub fn to_byte(self) -> u8 {
ALLOC_TIME_LATE | (self.code() << 2)
}
/// Decode the fill time from a version 3 fill value message's flags.
pub fn from_byte(flags: u8) -> Option<FillTime> {
match (flags >> 2) & 0x03 {
0 => Some(FillTime::Alloc),
1 => Some(FillTime::Never),
2 => Some(FillTime::IfSet),
_ => None,
}
}
fn code(self) -> u8 {
match self {
FillTime::Never => 0x02,
FillTime::Alloc => 0x0a,
FillTime::IfSet => 0x06,
FillTime::Alloc => 0,
FillTime::Never => 1,
FillTime::IfSet => 2,
}
}
}
/// Serialize a version 3 Fill Value message for a dataset of `dt`: the fill
/// time, and the user-defined fill value if there is one (bit 5).
pub(crate) fn fill_value_message(
fill_time: FillTime,
value: Option<&[u8]>,
dt: &Datatype,
) -> Result<Vec<u8>, crate::error::FormatError> {
let mut msg = vec![3, fill_time.to_byte()];
if let Some(value) = value {
if matches!(dt, Datatype::VariableLength { .. }) {
return Err(crate::error::FormatError::SerializationError(
"a fill value for a variable-length datatype is not supported".into(),
));
}
if value.len() != dt.type_size() as usize {
return Err(crate::error::FormatError::DataSizeMismatch {
expected: dt.type_size() as usize,
actual: value.len(),
});
}
msg[1] |= 0x20; // fill value defined
msg.extend_from_slice(&(value.len() as u32).to_le_bytes());
msg.extend_from_slice(value);
}
Ok(msg)
}
// ---- Datatype constructors ----
pub fn make_f64_type() -> Datatype {
@@ -56,6 +108,21 @@ pub fn make_f64_type() -> Datatype {
}
}
/// IEEE-754 half precision (binary16), little-endian — numpy's `float16`.
pub fn make_f16_type() -> Datatype {
Datatype::FloatingPoint {
size: 2,
byte_order: DatatypeByteOrder::LittleEndian,
bit_offset: 0,
bit_precision: 16,
exponent_location: 10,
exponent_size: 5,
mantissa_location: 0,
mantissa_size: 10,
exponent_bias: 15,
}
}
pub fn make_f32_type() -> Datatype {
Datatype::FloatingPoint {
size: 4,
@@ -317,7 +384,11 @@ pub(crate) fn build_attr_message(name: &str, value: &AttrValue) -> AttributeMess
raw_data: data.clone(),
},
AttrValue::String(s) => {
let bytes = s.as_bytes();
// A fixed-length string type must be at least 1 byte: libhdf5
// rejects size 0 ("invalid datatype size") and with it every
// attribute on the object. h5py stores "" as one NUL byte.
let mut bytes = s.as_bytes().to_vec();
bytes.resize(bytes.len().max(1), 0);
AttributeMessage {
name: name.to_string(),
datatype: Datatype::String {
@@ -326,11 +397,12 @@ pub(crate) fn build_attr_message(name: &str, value: &AttrValue) -> AttributeMess
charset: CharacterSet::Utf8,
},
dataspace: scalar_ds(),
raw_data: bytes.to_vec(),
raw_data: bytes,
}
}
AttrValue::StringArray(arr) => {
let max_len = arr.iter().map(|s| s.len()).max().unwrap_or(0);
// At least 1 byte per element, as for a single string.
let max_len = arr.iter().map(|s| s.len()).max().unwrap_or(0).max(1);
let mut raw = Vec::new();
for s in arr {
let mut b = s.as_bytes().to_vec();
@@ -416,8 +488,10 @@ pub struct DatasetBuilder {
pub(crate) data: Option<Vec<u8>>,
pub(crate) attrs: Vec<(String, AttrValue)>,
pub(crate) chunk_options: ChunkOptions,
/// Controls when fill values are written. Default is `FillTime::Alloc`.
/// Controls when fill values are written. Default is `FillTime::IfSet`.
pub(crate) fill_time: FillTime,
/// User-defined fill value: one element's bytes, as stored.
pub(crate) fill_value: Option<Vec<u8>>,
/// Use compact (inline) storage: data is stored in the object header.
/// Only valid when raw data is <= 65536 bytes and dataset is not chunked.
pub(crate) compact: bool,
@@ -444,6 +518,7 @@ impl DatasetBuilder {
attrs: Vec::new(),
chunk_options: ChunkOptions::default(),
fill_time: FillTime::default(),
fill_value: None,
compact: false,
alignment: 0,
virtual_sources: None,
@@ -478,6 +553,24 @@ impl DatasetBuilder {
self
}
/// Store `data` as IEEE half precision (numpy `float16`), rounding each
/// value to the nearest half ([`crate::float16::f32_to_f16_bits`]).
/// Half the bytes of [`Self::with_f32_data`], at about three significant
/// decimal digits; values beyond ±65504 become ±infinity. Reading it back
/// with `read_f32` yields the rounded values exactly.
pub fn with_f16_data(&mut self, data: &[f32]) -> &mut Self {
self.datatype = Some(make_f16_type());
let mut b = Vec::with_capacity(data.len() * 2);
for &v in data {
b.extend_from_slice(&crate::float16::f32_to_f16_bits(v).to_le_bytes());
}
self.data = Some(b);
if self.shape.is_none() {
self.shape = Some(vec![data.len() as u64]);
}
self
}
pub fn with_i32_data(&mut self, data: &[i32]) -> &mut Self {
self.datatype = Some(make_i32_type());
let mut b = Vec::with_capacity(data.len() * 4);
@@ -638,7 +731,12 @@ impl DatasetBuilder {
self
}
/// Enable Pcodec lossless numerical compression (clawhdf5 filter ID 32023).
/// Enable Pcodec lossless numerical compression (private clawhdf5 filter
/// ID 480).
///
/// **Not interoperable:** pcodec has no registered HDF5 filter ID and no
/// libhdf5 plugin, so h5py and other HDF5 readers cannot read the
/// dataset — only clawhdf5 built with the `pcodec` feature can.
///
/// Pcodec achieves 30–94% better compression ratio than Zstd for f32/f64
/// columns at 1–5 GiB/s decompression speed (arXiv:2502.06112). Requires
@@ -682,10 +780,20 @@ impl DatasetBuilder {
self
}
/// Set the dataset's fill value: what readers return for storage that
/// was never written (e.g. after the dataset is extended). `value` is one
/// element's bytes as stored — the dataset datatype's size and byte order
/// (`(-1i32).to_le_bytes()` for an `i32` dataset). A size mismatch, or a
/// variable-length datatype, makes `finish` fail.
pub fn with_fill_value(&mut self, value: &[u8]) -> &mut Self {
self.fill_value = Some(value.to_vec());
self
}
/// Use compact (inline) storage for this dataset.
///
/// The raw data is stored directly in the dataset's object header rather
/// than as a separate data blob. Only effective when raw data <= 65536 bytes
/// than as a separate data blob. Only effective when raw data <= 65531 bytes
/// and the dataset is not chunked.
pub fn compact(&mut self) -> &mut Self {
self.compact = true;
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