feat(dict)!: entropy tables measured from the samples - #533
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Included review availability: This review used your included allowance. Your plan provides up to 1 included review per hour; 0 remain after this review. 📝 WalkthroughWalkthroughDictionary finalization now derives entropy tables from training samples. COVER and FastCOVER score full-size candidates before shrinking the selected result. The change also updates raw-content training, compression-level handling, size checks, CLI and C API integration, and related tests. ChangesDictionary training and finalization
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~60 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant SampleSet
participant Analysis
participant FrameCompressor
participant Recorder
SampleSet->>Analysis: Provide samples and sample sizes
Analysis->>FrameCompressor: Compress samples with candidate content
FrameCompressor->>Recorder: Capture matcher and block statistics
Recorder-->>Analysis: Return literal and sequence statistics
Analysis-->>SampleSet: Provide statistics for entropy-table construction
Merge Risk: 🔵 Low · up to Sample-derived dictionary finalization and the API changes look sound. One narrow gap remains: with a tight memory limit, the legacy trainer can still run on fewer than five samples. The README now documents this behavior, so the change is mergeable with owner awareness. Security Architecture ReviewSecurity architecture risk: 🟡 Moderate · up to The new training flow changes the bytes a dictionary can produce and may give different dictionaries the same automatically generated ID when they use the same content but different samples. Existing input and output checks limit the immediate security concern, but downstream identity and compatibility expectations are not established. Retained concerns
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Review comments at @zstd/src/bin/structured-zstd/main.rs:
- Around line 3622-3625: After calculating retained in the training-load flow,
enforce TRAINING_SAMPLES_MIN against that budget-retained count before calling
enough_samples, so legacy training cannot proceed with fewer than five samples.
Preserve enough_samples for its existing checks and report the retained count
with guidance to raise the memory limit or create smaller samples.
Review comments at @zstd/src/dictionary/samples.rs:
- Around line 64-72: Move the sample-splitting documentation from
`check_holds_dmer` to `split`, so it describes the correct method. Keep the
dmer-validation documentation attached to `check_holds_dmer`.
Review comments at @zstd/src/encoding/frame_compressor.rs:
- Line 2491: Gate the compress_known_into method with the dict-builder feature
so it is not compiled when its caller is absent; leave its visibility and
implementation unchanged.
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.github/scripts/run-benchmarks.sh.github/workflows/ci.ymlREADME.mdc-api/src/dict.rsc-api/src/error.rsc-api/src/tests.rszstd/benches/compare_ffi.rszstd/benches/dict_builder_fastcover.rszstd/src/bin/structured-zstd/main.rszstd/src/bin/structured-zstd/tests.rszstd/src/bit_io/bit_writer.rszstd/src/dictionary/cover.rszstd/src/dictionary/cover/tests.rszstd/src/dictionary/fastcover.rszstd/src/dictionary/fastcover/tests.rszstd/src/dictionary/finalize.rszstd/src/dictionary/finalize/tests.rszstd/src/dictionary/legacy.rszstd/src/dictionary/lmc.rszstd/src/dictionary/mod.rszstd/src/dictionary/reservoir.rszstd/src/dictionary/samples.rszstd/src/dictionary/selection.rszstd/src/dictionary/tests.rszstd/src/encoding/blocks/compressed.rszstd/src/encoding/blocks/mod.rszstd/src/encoding/frame_compressor.rszstd/src/fse/fse_encoder.rszstd/src/fse/tests.rszstd/src/huff0/huff0_encoder.rs
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`create_raw_dict_from_dir`, `_source` and `_slice` built their content with a reservoir-sampled local-maximum-coverage trainer. Measured on the repository files against upstream zstd's `--train` dictionary, held-out samples compressed by upstream zstd: it came out 1.38% larger at level 3, 1.80% at level 19 and 8.14% on 1 KiB samples, where the FastCOVER search is within 0.14% of upstream; one training on 1.4 MB took minutes where the search takes under a second. The three functions now return what `optimize_fastcover_dict` picks at its defaults, as `zstd --train` does, without entropy tables: a directory is one sample per file, and a corpus without sizes is cut into at least sixteen samples of at most 128 KiB. A corpus no larger than the dictionary is its own content, and one the trainer refuses gives its last `dict_size` bytes, as before for tiny corpora. The old trainer, its reservoir sampler and frequency estimate are removed. The frame compressor's dict-builder test now compresses a small frame, which a dictionary exists for: a long frame of near-identical lines compresses smaller without one, in upstream zstd too (245 against 261 bytes with this dictionary at level 1). Part of #128
The finalizer built its tables from the samples' raw bytes, counting byte values modulo each alphabet as if they were sequence codes. Such tables made a dictionary compress its own samples worse than its bare content did. - finalize_raw_dict follows upstream's ZDICT_analyzeEntropy: the first block of each sample is compressed with the content as a raw dictionary, and the literals, literal/match lengths and offset codes those blocks hold are counted; tables at upstream's logs (literals up to 11 bits, offsets 8, lengths 9), a mostly flat stand-in when literals are flat - blocks written raw teach nothing and are left out, as upstream does - the literals table is scaled to its longest code, as upstream writes it - FinalizeOptions carries the level (upstream's zParams.compressionLevel), used both for the analysis and for scoring candidates; CoverOptions no longer has one - MIN_TRAINED_DICT_SIZE is upstream's ZDICT_DICTSIZE_MIN, 256 - ZDICT_finalizeDictionary passes the sample sizes and honours compressionLevel Measured on 321 repository files, 16 KiB dictionaries, compressed with upstream zstd -3: the dictionary gap to upstream's went from +2.3..3.0% to +0.09..0.6% across --train, fixed COVER and fixed FastCOVER. BREAKING CHANGE: finalize_raw_dict takes the sample sizes; FinalizeOptions gains `level` and CoverOptions loses it; MIN_TRAINED_DICT_SIZE is 256. Part of #128
- the counting and selecting loops are monomorphised on the number of bytes a dmer hashes, once above the loop, instead of branching on d at every position - the window keeps each entering position's table index in a ring and reads it back as the position leaves; upstream hashes it a second time - reads and table indexing go through pointers: every position below nb_dmers has eight readable bytes and hashes below 2^f, both tables' length; the ring is bounded by the epoch, which a window never exceeds Dictionaries are byte-identical. x86 (runner2), fixed FastCOVER k=256 d=8 on 6420 samples, task-clock over three interleaved rounds: 5.29 s -> 4.98 s. Part of #128
The finalizer hinted each sample's own size, so every sample ran its own table sizes and the dictionary was indexed again for each one. Upstream analyses them all with the parameters of the average sample (ZSTD_getParams(level, averageSampleSize, dictSize)); doing the same keeps one set of tables for the pass and the dictionary resident between samples. x86 (runner2), fixed FastCOVER k=256 d=8 on 6420 samples, task-clock over three interleaved rounds: 4.99 s -> 4.38 s; the dictionary's total over the corpus at upstream -3 went from 1851466 to 1850248 (upstream's own: 1849508). Part of #128
Upstream's selection shrinks each candidate of its parameter search (and then never runs it). The size a dictionary is cut to is a separate choice from the k and d that built it, and cutting every candidate multiplies the whole search by the number of sizes tried: a zero-k, zero-d FastCOVER search with shrink took 56.6 s where the search alone takes a few. The search now keeps the winner's content and cuts only that dictionary. Part of #128
- Only a sample's first block reaches the entropy tables, as upstream's ZDICT_countEStats compresses MIN(128 KiB, window) bytes as one block. A sample longer than the window used to add every later block, including ones written raw. Once a frame has shown the window, the rest of each sample is not fed at all. Regression test: only_the_first_block_of_a_sample_is_counted. - Offset codes are counted with the repeat policy the frame's blocks used: at the fast strategy an offset equal to rep[1] or rep[2] is written explicitly, and the finalizer counted it as repeat 2 or 3. One function, uses_fast_offset_codes, now decides this for the block encoder, its size estimate and the finalizer. Regression test: offset_codes_follow_the_frames_repeat_policy. - The four table descriptions are written straight into the dictionary: the sequence tables are normalized and described without building encoder tables (write_ncount, shared with FSETable::write_table), and the literals table writes its description alone instead of encoding a symbol and parsing it back. - The finalizer's too-small refusal carries TrainingError::DictionaryTooSmall and is checked first, as upstream does, so ZDICT_finalizeDictionary returns dstSize_tooSmall.
The entropy analysis runs upstream's parameters for the average sample and the content, ZSTD_getParams(level, averageSampleSize, dictSize), set explicitly, and hints the frames as a source of both together, the size upstream fits the window to. A frame with a dictionary fits its window to the source alone, so hinting the average sample cut the analysed block to the sample's window: on the repository files the fixed COVER dictionary lost 999 bytes against the one before. Matcher gains apply_parameters, a default no-op the built-in matcher implements, so FrameCompressor::set_parameters works with any matcher. Regression test: the_first_block_spans_the_window_of_sample_and_content.
The entropy analysis counts every block of a sample again, and now leaves out each block written raw or as one repeated byte: its bytes are no literals of any compressed block and it holds no sequences. The recorder keeps one entry per block, matched or skipped, and the frame's block headers say which were compressed. Counting the first block alone, as upstream's ZDICT_countEStats does, measured worse on the repository files: at our window the samples compressed 0.013% larger in total across fixed COVER, fixed FastCOVER and the default search, at upstream's window (average sample plus content, set with its parameters) 0.05% larger. This replaces both, including the Matcher::apply_parameters hook the second one needed. Regression test: a_block_written_raw_is_not_counted.
Writing and sizing an NCount description copied the 256 probabilities of a built table into an array first, so every new sequence table a block emitted, and every header priced for the table-mode choice, paid a 1 KiB copy. The writer and the size count now read the probabilities through an accessor, from the table itself or from the normalized counts.
- The finalizer writes over a buffer the caller keeps, and the evaluator finalizes each candidate, and each shrunk one, over two buffers it holds; the best-so-far copies a candidate's dictionary and content only when it wins. Dictionaries are byte-identical. - The candidate copied into the encoder dictionary stays a copy: under callgrind the copy and the dictionary's preparation are 0.02% of a default training run, against 52.7% for compressing the samples with it.
- The entropy analysis keeps its compressor, frame buffer and block kinds on the evaluator: between candidates only the dictionary changes, so the matcher and encoder scratch are no longer rebuilt for each one. The compressor is rebuilt when the level changes. - Each sample's frame is written straight into the kept buffer (compress_known_into: the header first, the length being known, then the blocks), without the streaming path's block accumulator and copy. - Offset codes are counted into a fixed array over every code, as upstream zstd's offcodeCount is, and described up to the alphabet's bound: no branch per sequence, no allocation per analysis. Dictionaries are byte-identical for --train and --train-cover on a frozen corpus. Time on an M1 moved within noise (--train -1.0%, --train-cover +1.3% by minimum, both under what two builds resolve).
The finalizer built the literals code at the length limit and, when its longest code fell short of it, built it again at that length only to describe it there. Below the limit the height limiter does nothing, so both builds give the same code lengths and the second only lowers every weight by the same step: `build_limited_in` now does that step on the first build's weights, which is the table upstream zstd writes from the `maxNbBits` its build returns. The weights, the tree nodes and the table's buffers live in a `WeightScratch` the analysis keeps from one candidate to the next, so a parameter search allocates none of them per candidate. Dictionaries are byte-identical to the previous head for --train, COVER, FastCOVER and FastCOVER with shrink at levels 1, 3, 9 and 19. A test checks the code built under the limit against the code built at its longest length, over a scratch holding another table.
The literal counts the finalizer sums over every sample fed the Huffman builder unchecked: past 2^32 literals, which a direct finalize over large samples can reach, the tree's u32 node counts wrapped in release and panicked in debug. The counts are now halved, rounding up so every symbol that occurred stays in the code, until they fit a node, and `build_limited_in` runs the entry check the other builders run. Regression test: literal_counts_past_a_tree_node_still_build_a_code. A search keeps the winner's content only when the winner will be shrunk; the shrink setting now lives in `Best`, which ranks candidates at full size and cuts only the winner, as `CoverOptions::shrink` documents. Dictionaries are byte-identical for --train, FastCOVER, COVER with a search and COVER with shrink.
`finalize_raw_dict` built its sample set, walking every size and allocating the offsets, before it refused a dictionary under 256 bytes, and `ZDICT_finalizeDictionary` summed the sample sizes first too: an impossible request over a large corpus paid for the walk, and sizes that did not add up were reported instead of the size. Upstream zstd checks the capacity before anything else. The rule is the codec's `check_finalize_dict_size`, which `finalize_raw_dict` runs first and the C ABI runs before it reads any argument. Regression tests: finalize_raw_dict_refuses_an_undersized_dictionary_before_the_samples, and an overflowing size list in zdict_trainers_report_upstreams_error_codes.
- `FrameCompressor::compress_known_into` has only the dictionary finalizer as a caller, so a build without `dict-builder` failed its dead-code lint; it is compiled with that feature only - the paragraph describing `SampleSet::split` sat above `check_holds_dmer`; it now documents `split` - the README and the loader say where the five-sample floor applies: to the samples the files make, as upstream's command checks it (dibio.c); COVER and FastCOVER check what the split and the memory limit keep, and the legacy trainer, as upstream's, trains on whatever the limit keeps Part of #128
The raw-dictionary path ran the FastCOVER search, then built a second context from the parameters it chose and selected the same segments again: another corpus-wide dmer count, another 2^20-entry frequency table and another selection, for content the search already held. The search now takes what to keep of its winner: its dictionary, cut down when shrinking, or the raw content it was finalized from. The raw path asks for the content and gets it straight from the search, and a content search no longer copies each winner's finalized dictionary. The content is unchanged; raw_content_is_the_fastcover_search_winner checks it against the dictionary the search finalizes. Part of #128
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At btopt and above with a window of 128 KiB or more, the frame the analysis compresses could cut a matched block into several blocks after matching. The recorder sees one block per matcher call, so the pieces could not be told apart, and a frame with any raw piece had none of its blocks counted: a 128 KiB sample of 16 KiB of log lines and 112 KiB of noise at level 19 went out as two compressed pieces and a raw one and taught the tables nothing. The analysis compressor now never cuts after matching, as upstream's analysis compresses a single block (`ZDICT_countEStats`, `ZSTD_compressBlock`), so every written block is one recorded block and is counted by its own kind. Regression test: a_block_mixing_text_and_noise_is_counted. The literals table's description is encoded into the table's own buffer with the analysis' FSE table before it is written, both kept from one candidate to the next, instead of into a fresh buffer and table per candidate. Dictionaries on the repository files and the decodecorpus files are unchanged at levels 3 and 19, where no sample reaches a split. Part of #128
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## 🤖 New release * `structured-zstd`: 0.0.57 -> 0.0.58 <details><summary><i><b>Changelog</b></i></summary><p> <blockquote> ## [0.0.58](v0.0.57...v0.0.58) - 2026-10-01 ### Added - *(dict)* [**breaking**] entropy tables measured from the samples ([#533](#533)) - *(dict)* [**breaking**] upstream COVER and sample-aware trainers ([#532](#532)) - cap the CPU kernel tier (--cpu, set_cpu_ceiling) ([#537](#537)) ### Performance - *(encode)* code Fast-band offsets as the sequences are collected ([#547](#547)) - *(encode)* optimal parser speed on small inputs ([#546](#546)) - *(encode)* make the classifier and Fast scans placement-stable ([#544](#544)) - *(decode)* take the wildcopy width from the kernel type ([#540](#540)) - *(encode)* tag Fast and dfast hash slots to skip colliding candidates ([#539](#539)) - *(encode)* [**breaking**] gather literal runs after matching, report sequences by length ([#538](#538)) </blockquote> </p></details> --- This PR was generated with [release-plz](https://github.com/release-plz/release-plz/). Co-authored-by: sw-release-bot[bot] <255865126+sw-release-bot[bot]@users.noreply.github.com>
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Summary
ZDICT_analyzeEntropydoes: each sample is compressed with the content as a raw dictionary and the literals and sequence codes its compressed blocks produce are counted. The previous tables, built from raw byte values, made a dictionary compress its own samples worse than its bare content.shrinkapplied to the search's winner only.create_raw_dict_from_dir,_sourceand_slicereturn the contentzstd --train's FastCOVER search picks instead of running the reservoir-sampled local-maximum-coverage trainer, which is removed: on the repository files that trainer's dictionaries compressed held-out samples 1.4-1.8% worse than upstream's, 8.1% worse on 1 KiB samples, and took minutes where the search takes under a second.Changes
dictionary: raw content without sample sizes is cut into at least sixteen samples of at most 128 KiB (a directory gives one per file) and searched asoptimize_fastcover_dictsearches at its defaults; a corpus no larger than the dictionary is its own content, and one the trainer refuses gives its lastdict_sizebytes. The reservoir sampler and the k-mer frequency estimate go with the old trainer.dictionary::finalize: statistics collected through a recording matcher around the production one, block by block, never cut after matching, as upstream's single-block analysis; a block written raw or as one repeated byte is left out; offset codes counted with the repeat policy the block used (the fast strategy writes deeper repeats explicitly); tables at upstream's logs (literals up to 11 bits, offsets 8, lengths 9); a mostly flat stand-in when literals are flat; the literals table scaled to its longest code; literal counts summed past what a Huffman tree node holds are halved to fit, keeping every symbol that occurred.finalize_raw_dicttakes the sample sizes;FinalizeOptionscarries the level (upstream'szParams.compressionLevel), used for the analysis and for scoring;MIN_TRAINED_DICT_SIZEis upstream's 256; a size too small is refused first, before the samples are walked (check_finalize_dict_size), and carriesTrainingError::DictionaryTooSmall.ZDICT_finalizeDictionarypasses the sample sizes, honourscompressionLeveland returnsdstSize_tooSmallfor a buffer under 256 bytes; the CLI builds dictionaries for its-#level.Measurements
x86_64 runner (Xeon, bench profile), 315 repository files, 16 KiB dictionaries, every dictionary evaluated by compressing all files with upstream zstd 1.5.7
-3; upstream trains with-T1. Time is task-clock of the training command.--train--train-cover--train-cover=k=256,d=8--train-fastcover=k=256,d=8--train-fastcover=shrink--train-cover=k=256,d=8,shrinkThe
shrinkrow is not like for like: upstream's optimizer parsesshrinkand never applies it, so its dictionary is the full 16 KiB, while ours is cut to about half; the gap is the price of the smaller dictionary, within the regressionshrinkallows on the scoring samples.Across levels and sample shapes (M1; the files split in half,
--train -Lon one half, the held-out half compressed by upstream zstd at the same level; bytes are the held-out total, gaps against upstream's dictionary; "before" is #532 without this change):On the decodecorpus files no dictionary helps anyone (the bare frames are smallest), so their rows show only that no level breaks.
Counting only a sample's first block, the policy of upstream's analysis, was measured on the same files and rejected: at our window the evaluated totals grew by 0.013% summed over fixed COVER, fixed FastCOVER and
--train, at upstream's window (average sample plus content) by 0.05%. Leaving out raw blocks and writing the descriptions directly produce byte-identical dictionaries on these files (no sample there has a raw block), at unchanged training time. The remaining FastCOVER time gap is the dictionary encoder on small frames, which the scoring pass runs.Testing
Tests, doc tests, clippy and formatting pass on macOS; lints for the no-std, i686 and thumbv7em targets pass.
BREAKING CHANGE:
finalize_raw_dicttakes the sample sizes;FinalizeOptionsgainslevelandCoverOptionsloses it;MIN_TRAINED_DICT_SIZEis 256.Part of #128