#utility #node #task #core #top-down

bin+lib stitch_core

A scalable abstraction learning library

1 unstable release

0.1.0 Nov 5, 2022

MIT license

4.5K SLoC

Rust 3K SLoC // 0.1% comments Python 1K SLoC // 0.1% comments Shell 148 SLoC // 0.2% comments

A pre-print of Stitch is available here.

Tutorial coming soon!



Run cargo run --release --bin=compress -- data/cogsci/nuts-bolts.json --max-arity=3 --iterations=10

In less than a second this should produce an output like:

=======Compression Summary=======
Found 10 inventions
Cost Improvement: (11.93x better) 1919558 -> 160946
fn_0 (1.78x wrt orig): utility: 837792 | final_cost: 1079238 | 1.78x | uses: 320 | body: [fn_0 arity=2: (T (repeat (T l (M 1 0 -0.5 (/ 0.5 (tan (/ pi #1))))) #1 (M 1 (/ (* 2 pi) #1) 0 0)) (M #0 0 0 0))]
fn_1 (3.81x wrt orig): utility: 572767 | final_cost: 503538 | 2.14x | uses: 190 | body: [fn_1 arity=3: (repeat (T (T #2 (M 0.5 0 0 0)) (M 1 0 (* #1 (cos (/ pi 4))) (* #1 (sin (/ pi 4))))) #0 (M 1 (/ (* 2 pi) #0) 0 0))]
fn_2 (6.06x wrt orig): utility: 185436 | final_cost: 316890 | 1.59x | uses: 168 | body: [fn_2 arity=1: (T (T c (M 2 0 0 0)) (M #0 0 0 0))]
fn_3 (7.18x wrt orig): utility: 48984 | final_cost: 267198 | 1.19x | uses: 82 | body: [fn_3 arity=2: (C #1 (T r (M #0 0 0 0)))]
fn_4 (8.29x wrt orig): utility: 35046 | final_cost: 231646 | 1.15x | uses: 88 | body: [fn_4 arity=2: (C (fn_0 4 #1) (fn_0 #0 6))]
fn_5 (9.04x wrt orig): utility: 18885 | final_cost: 212456 | 1.09x | uses: 95 | body: [fn_5 arity=3: (C #2 (fn_1 #1 1.5 #0))]
fn_6 (9.93x wrt orig): utility: 18885 | final_cost: 193266 | 1.10x | uses: 95 | body: [fn_6 arity=3: (C #2 (fn_1 #1 3 #0))]
fn_7 (10.53x wrt orig): utility: 10604 | final_cost: 182358 | 1.06x | uses: 54 | body: [fn_7 arity=2: (C #1 (fn_0 #0 6))]
fn_8 (11.20x wrt orig): utility: 10503 | final_cost: 171450 | 1.06x | uses: 36 | body: [fn_8 arity=2: (C (fn_0 4 #1) (fn_2 #0))]
fn_9 (11.93x wrt orig): utility: 10202 | final_cost: 160946 | 1.07x | uses: 52 | body: [fn_9 arity=0: (fn_4 4.25 6)]
Time: 227ms

Brief guide to reading this:

  • fn_0 is the autogenerated name of the abstraction
  • (1.78x wrt orig) means the resulting compressed programs using inv0 were 1.78x smaller than the original programs, while later on in the line the other 1.78x is the compression relative to the previous step (for the first step they are the same).
  • utility: 836528 this is a measure of how many much smaller the program got after rewriting it in terms of the new primitives (divide by 100 to get the approximate number of primitives that were removed)
  • uses: 320 the abstraction was useful in 320 places in the set of programs
  • Note that in these abstraction #i is used for abstraction variables and $i for original program variables.

Common command-line arguments

  • --max-arity=2 or -a2 controls max arity of abstraction found (default is 2)
  • --iterations=10 or -i10 controls how many iterations of compression to run. Each iteration produces one abstraction (which can build on the previous ones)
  • --threads=10 or -t10 is a quick way to boost performance by multithreading (default is 1)

All command-line arguments

From cargo run --release --bin=compress -- --help

    <FILE>    json file to read compression input programs from

    -a, --max-arity <MAX_ARITY>
            max arity of abstractions to find (will find all from 0 to this number inclusive)
            [default: 2]

            extracts argument values from the json; specifically assumes a key value pair like
            "stitch_args": "data/dc/logo_iteration_1_stitchargs.json -a3 -t8 --fmt=dreamcoder
            --dreamcoder-drop-last --no-mismatch-check", in the toplevel dictionary of the json. All
            other commandline args get discarded when you specify this option

    -b, --batch <BATCH>
            how many worklist items a thread will take at once [default: 1]

            anything related to running a dreamcoder comparison

            threads will autoadjust how large their batches are based on the worklist size

        --fmt <FMT>
            the format of the input file, e.g. 'programs-list' for a simple JSON array of programs
            or 'dreamcoder' for a JSON in the style expected by the original dreamcoder codebase.
            See [formats.rs] for options or to add new ones [default: programs-list] [possible
            values: dreamcoder, programs-list, split-programs-list]

            for debugging: prunes all branches except the one that leads to the `--track`

    -h, --help
            Print help information

        --hole-choice <HOLE_CHOICE>
            Method for choosing hole to expand at each step, doesn't have a huge effect [default:
            depth-first] [possible values: random, breadth-first, depth-first, max-largest-subset,
            high-entropy, low-entropy, max-cost, min-cost, many-groups, few-groups, few-apps]

    -i, --iterations <ITERATIONS>
            Number of iterations to run compression for (number of inventions to find) [default: 3]

    -n, --inv-candidates <INV_CANDIDATES>
            Number of invention candidates compression_step should return in a *single* step. Note
            that these will be the top n optimal candidates modulo subsumption pruning (and the top-
            1  is guaranteed to be globally optimal) [default: 1]

            disables the safety check for the utility being correct; you only want to do this if you
            truly dont mind unsoundness for a minute

            disable all optimizations

            disable the arity zero priming optimization

            disable the force multiuse pruning optimization

            disable the free variable pruning optimization

            disable the single task pruning optimization

            disable the single structurally hashed subtree match pruning

            disable the upper bound pruning optimization

            disable the useless abstraction pruning optimization

            makes it so utility is based purely on corpus size without adding in the abstraction

            Disable stat logging - note that stat logging in multithreading requires taking a mutex
            so it can be a source of slowdown in the massively multithreaded case, hence this flag
            to disable it

            makes it so inventions cant start with a lambda at the top

    -o, --out <OUT>
            json output file [default: out/out.json]

        --print-stats <PRINT_STATS>
            print stats this often (0 means never) [default: 0]

    -r, --show-rewritten
            print out programs rewritten under abstraction

            whenever you finish an invention do a full rewrite to check that rewriting doesnt raise
            a cost mismatch exception

        --save-rewritten <SAVE_REWRITTEN>
            saves the rewritten frontiers in an input-readable format

            shuffle order of set of inventions

    -t, --threads <THREADS>
            number of threads (no parallelism if set to 1) [default: 1]

        --track <TRACK>
            for debugging: pattern or abstraction to track

        --truncate <TRUNCATE>
            truncate set of inventions to include only this many (happens after shuffle if shuffle
            is also specified)

            calculate utility exhaustively by performing a full rewrite; mainly used when cost
            mismatches are happening and we need something slow but accurate

            prints whenever a new best abstraction is found

            prints every worklist item as it is processed (will slow things down a ton due to
            rendering out expressins)

Disabling optimizations

cargo run --release --bin=compress -- data/cogsci/nuts-bolts.json --no-opt

Or see the other commandline arguments beginning with --no-opt- to disable specific optimizations

Python Bindings

Currently initial Python bindings are offered.

  • Build the bindings by running ./gen_bindings_osx.sh or ./gen_bindings_linux.sh depending on your OS (they will be added to bindings/)
    • Tell me or open an issue if this command doesn't work! It may vary by OS and the current commands may be overfit to my computers.
  • Add the stitch/bindings/ folder to your $PYTHONPATH, for example by adding export PYTHONPATH="$PYTHONPATH:path/to/stitch/bindings/" to your ~/.bashrc or however you do it with your particular shell / venv. This will mean the stitch.so file is in your python path which will let you import it.
  • Launch python and try to import stitch (nothing should be printed if this works)
  • As a simple example run the Python code import stitch,json; result = json.loads(stitch.compression(["(a a a)", "(b b b)"], iterations=1, max_arity=2)); print("Result:", result) and it should find the (#0 #0 #0) abstraction.
  • Note that currently it outputs a large python dictionary similar to the usual out/out.json output of stitch.
  • There are a lot more keyword arguments available (full list in examples/stitch.rs which is where the bindings live since keeping things in examples/ is a workaround for having a project generate a cdylib for Python bindings in addition to normal Rust/). Basically everything that you would find in cargo run --release --bin=compress -- --help is included.


  • --save-baseline=main saves a named baseline (comparing against a past version of it if it exists, then overwriting it)
  • --load-baseline=feature means don't run any benchmarks just load the file as if it's a result that you just produced
  • --baseline=master overrides which benchmark we're going to compare against
  • --bench=compress_bench avoids the "unrecognized option" error detailed here



install if you havent: cargo install flamegraph cargo flamegraph --root --open --deterministic --output=out/flamegraph.svg --bin=compress -- data/cogsci/nuts-bolts.json


This work is supported by the National Science Foundation under Grant No. 1918839 Understanding the World Through Code http://www.neurosymbolic.org/

This work is supported in part by the Defense Advanced Research Projects Agency (DARPA) under the program Symbiotic Design for Cyber Physical Systems (SDCPS) Contract FA8750-20-C-0542 (Systemic Generative Engineering). The views, opinions, and/or findings expressed are those of the author(s) and do not necessarily reflect the view of DARPA.


~120K SLoC