Something that I’m tinkering with is making a proof assistant in python.

It’s very preliminary, but I’ve place some ideas here. I call it Knuckledragger (thanks Zach!). I haven’t done much, but it’s too much done without writing about it. So here we are. Write early, write often.

There are a few reasons to do this in python:

• Huge amounts of tooling (Jupyter, documentation, editors etc)
• Huge numbers of packages (z3py, sympy, cvxpy, numpy, scipy, networkx, sage stuff, etc)
• Huge userbase and familiarity
• It is huge burden for a new user to learn formal mathematical proof, and a new language.

It is relatively unattempted (or at least wildly successful?) to make a proof assistant in a standard language (Let’s say >1% TIOBE index). It is not unknown however. Some examples of python based ITP are HolPy, Boole, Prove-It.

It is possible that I will be driven into agreeing using one of the existing systems (in python or otherwise) is the right thing to do. I’d consider that a success. I’m enjoying the process very much irregardless.

Something I’ve experienced in using automated logic solvers (z3, eprover, egglog) is that one can perform nice big steps in a proof like rearranging expressions or facts about linear arithmetic, but that there are not facilities for chaining these results together. You also can’t express axiom schema (like induction!) or definitional mechanisms. You can only bring the edges close together in a file and wave your hands. I think this semi-automated corner of the design space is interesting. One puts all the meat in the solvers (and trusting that z3, eprover, etc are basically correct, de bruijn be damned) and has a light layer of trustable chaining and metaprogramming on top. This is not an unknown idea. It has been around since the very first solvers in the 50s and is similar to the approaches of F*, Liquid Haskell, and Dafny.

A traditional inference chaining design can be seen in LCF style kernels, where inference rules are implemented as functions taking in an abstract datatype theorem and outputting a new theorem. I found it a revelation to read chapter 6 of John Harrison’s book on this topic. You can look inside theorem to see the formula formula_of_proof : theorem -> formula but there is not explicit function to do the opposite (except an unsafe_axiom : formula -> theorem primitive.). In a language like Ocaml, the immutability and type system give comfort that you can’t make bad deductions through accidentally mutating something you shouldn’t have.

Python is extremely dynamic, mutable, and it is hard to protect your theorems. There are a few approaches to this chaining mechanism amenable to python. The ability to make the distinction between proven theorems and unproven formula is crucial.

• Hidden constructors and frozen. This is most like the LCF approach. It’s fine. A hidden bool flag differentiating proven theorems from unproven formula could work.
• A central authority that stores the known theorems. Proofs are keys into this database. The keys may be names or just indices into some list
• Crypto signing. If the proof authority maintains a secret key it uses to sign theorems from inferences it permits, it can check these crypto signatures when the theorem comes back in.
• Explicit checkable proof objects that you hand back to the user. Propositions as types is one choice of how to do this. A simpler perhaps clunkier choice is to just have a tree recording what theorems depended on which theorems. For example something like proof_db = [(a, "axiom"), (a => b, "axiom"), (b => c, "axiom"), (b,("z3infer", [0,1])), (c, ("z3infer", 3,2)), ...] that is filled out by interacting with an api. It could also record whatever “proof” object the solver in question happens to spit out in addition to the dependencies. These proof objects could be seen as traces of the interactions above between the authority and the user. The proof database of the authority with some annotations can also be seen as this data structure. You then have to also write a checker (which is in a sense a replay of the interaction).

As a toy example to demonstrate some ideas, here I prove the commutativity of Peano addition using an induction axiom schema. This is not possible for Z3 on it’s own.

Of course, this is very easy using built in arithmetic.

from z3 import *
x,y = Ints("x y")
prove(x + y == y + x)


This uses the “crypto” form I was referring to above which signs theorem via a secret key. check confirms the hash, trust is a primitive to make axioms. infer is a mega inference rule that admits anything that z3 can prove.

In order to describe Natural number induction, we have some choices. We could either use a higher order axioms (since Z3 does support simply typed higher order functions called “Arrays”) or we can use an axiom schema. An axiom schema is a family of axioms. We can implement such a family as an axiom producing function (another interesting option not explored here is as a generator of axioms, which would enable a brute force automation technique).

The commutativity of addition is a little funky to prove because it depends on the structure of hw you defined addition, which feels odd to naive mathematics. Indeed it feels odd from a certain perspective to say you “prover” commutativity. Isn’t that part of the definition of addition? Well, here we define addition only be using structural recursion on the first argument, so no it is not. This is a formal and technical point sadly. You can compare what is done here to Coq for example https://softwarefoundations.cis.upenn.edu/lf-current/Induction.html.

First we prove that 0 + x = x + 0 which again isn’t quite obvious. Then we use that as a lemma in the full proof.

from typing import Any, Tuple, List
from z3 import *

Form = Any
Thm = Tuple[int, Form]

BoolRef.__and__ = lambda self, other: And(self, other)
BoolRef.__or__ = lambda self, other: Or(self, other)
BoolRef.__invert__ = lambda self: Not(self)
def QForAll(vars, hyp, conc):
"""Quantified ForAll helper"""
return ForAll(vars, Implies(hyp, conc))

#########################
### Kernel
#########################
def check(thm: Thm):
"""Check that a theorem hashes against secret key"""
hsh, form = thm
assert hsh == hash(("secret", form))

def trust(form: Form) -> Thm:
"""Trust a formula as a theorem by adding a hash"""
return hash(("secret", form)), form

def infer(hyps: List[Thm], conc: Form, timeout=1000) -> Thm:
"""Use Z3 as giant inference step"""
s = Solver()
for hyp in hyps:
check(hyp)
s.set("timeout", timeout)
res = s.check()
if res != z3.unsat:
print(s.sexpr())
if res == z3.sat:
print(s.model())
assert False, res
return trust(conc)

################################
### Peano Arithmetic
################################

# Z3py algebraic datatype of natural numbers
Nat = Datatype("Nat")
Nat.declare("zero")
Nat.declare("succ", ("pred", Nat))
Nat = Nat.create()
print(Nat.succ(Nat.zero))

# Peano induction axiom schema
def induct(P : Form) -> Thm:
print(P.sort())
assert P.sort().name() == "Array"
assert P.sort().domain() == Nat
assert P.sort().range() == BoolSort()
n = FreshConst(Nat)
hyp = P[Nat.zero] & QForAll([n], P[n], P[Nat.succ(n)])
#------------------------------------------
conc =  ForAll([n], P[n])
return trust(Implies(hyp, conc))

x,y = Consts("x y", Nat)
# We just admit the definition of add, but really we need a termination checking mechanism.

base = infer([], P[Nat.zero])
# Alternative proof letting it figure out base and ind



# Bits and Bobbles

Thanks to Zach Tatlock for inspiration on the name!

I would love for knuckledragger to target relatively mundane engineering mathematics and computer science. I think the high falutin’ mathematical community is better served by higher powered systems. Type theory and set theory are too high a bar for most. I want things that a former physicist could appreciated, and I think I have the context for that.

Metaprogramming in most systems is a janky mess, because building a metaprogramming system is a huge engineering undertaking. HOL Light seems like it has it right by directly just working in OCaml. That may be part of it’s secret sauce (beside just being Harrison) to getting so much distance. Using a well supported existing language s the right call. Maybe Lean can pull off using itself as it’s metaprogramming system. That’d be great. There’s a lot of momentum there and Leo de Moura is a monster.

I fear using Z3 will fail complex quantifier reasoning. But I then may cash out to eprover/vampire which have good quantifier reasoning. Z3 is super useful, the python bindings are fantastic.

A Tactic system can be orchestrated around the raw capabilities above without sacrificing the soundness of the kernel. Isar style, backwards goal, and calc tactics all seem fun. At what point of complexity of tactics is the kernel kind of irrelevant?

I’m a weirdo that really likes funky programming languages and paradigms (functional, logic, dependently typed, etc). I have a lot of difficulty bringing my non PL friends on board anything that isn’t python. It is a lost cause in my opinion. Do not expect the world at large to start using functional programming languages. Maybe we don’t even actually want that. Mathematical Logic through Python

ACL2 is a good place to look for inspiration. I don’t want the fanciest most expressive logic in the world. Python and lisp do have a certain kinship (see for example)

It’s interesting to compare what I’m doing here with myself in 2019. On one hand, I feel like I’ve made no progress at all. On the other, think I’m just cargo culting the experience of using coq tactics in this post without a strong mental model of what it means to have a proof. On the other hand, to have the tactic system be the kernel is an interesting design that may not occur to me today because I’m too indoctrinated at this point.

Exporting proof certificates from different solvers is a of course interesting and would be preferable to just trusting them. The situation has not settled there though. There’s dedukti, alethe, z3’s current format, it’s in progress format, datalog provenance, egg/egglog proof data, twee proofs, metamath, and TPTP proof output. None of these are dominant or play that nice with each other. Maybe recording whatever proof output in the proof db is ok. DRAT for SAT is fairly standard.

Sometimes solvers output objects that are very useful. Sympy can output an antiderivative, checking it is indeed an antiderivatve is relatively easy. Likewise for SAT solutions, dual vectors in linear programming and some others.

CPressey also has some very interesting python assistants https://github.com/catseye/Eqthy and others. https://codeberg.org/catseye/Philomath#philomath philomath is a C based one! That’s an amusing idea.

It amuses me that chaining together automated theorem provers + a proof db / certificate system is also buildable in bash. Why not? It’d be kind of nice even. Bash is the nicest language to call other programs in / has god support for splicing.

A blog post a week this year? No matter how raw and uneditted? That’d be cool.

I’ve got lots of little examples I want to explore using equational reasoning, set theory, calculus, ordinals, algebra, metatheory, software verification, etc. Fun fun!!!

# Blah Blah Blah

## A little propositional logic without usig z3

from typing import Any, Tuple
Fm = Any #typing.Union[tuple("->", Fm, Fm), tuple("-", Fm), str]
Thm = Tuple[int, Fm]

def trust(a : Fm) -> Thm:
return hash(("secret",a)), a # Use a crypto hash function here

def check(a : Thm):
hsh, form = a
assert hsh == hash(("secret",form))

def modus(a : Thm, ab : Thm) -> Thm:
check(ab)
check(a)
_, a = a
_, (arr, a1, b) = ab
assert arr == "->"
assert a1 == a
return trust(b)

# axiom schema of propositional logic
def axiom1(A : Fm, B : Fm) -> Thm:
return trust(("->", A, ("->", B, A)))

def axiom2(A : Fm, B : Fm, C : Fm) -> Thm:
return trust(("->", ("->", A, ("->", B, C)), ("->", ("->", A, B), ("->", A, C))))

def axiom3(A, B):
return trust(("->", ("->", ("-", A), ("-", B)), ("->", B, A)))

def pprint_fm(a):
if a[0] == "->":
return f"( {pprint_fm(a[1])} -> {pprint_fm(a[2])})"
if a[0] == "-":
return f"(- {pprint_fm(a[1])})"
else:
return a

def pprint_pf(a):
print(pprint_fm(a[1]))

A = "A"
pf = []
pf.append(axiom1(A, A))
pf.append(axiom1(A, ("->", A, A)))
pf.append(axiom2(A, ("->", A, A), A))
pf.append(modus(pf[-2], pf[-1]))
pf.append(modus(pf[0], pf[-1]))

import pprint
pprint.pprint(pf)
for x in pf:
pprint_fm(x)

# A super simple "tactic"
rev_modus = lambda ab, a: modus(a, ab)
def a_to_a(A):
pf = []
pf.append(axiom1(A, A))
pf.append(axiom1(A, ("->", A, A)))
pf.append(axiom2(A, ("->", A, A), A))
pf.append(modus(pf[-2], pf[-1]))
pf.append(modus(pf[0], pf[-1]))
return pf[-1]

# not having variable really stinks.


https://us.metamath.org/mpeuni/idALT.html Metamath is a good example of this stuff.

## Proof server

An amusing idea related to the crypto signing is to have a proof server that anyway could access over an api. Python of course makes making an http server easy. This makes it more clear where the kernel is since it is in an entirely separate computer rather than being in process.

It also could make a for a fun game.

This opens up the question of how you could make a proof system hardened against cyber security attacks like buffer overflows, etc etc. A truly antagonistic adversary. There are always layers of trust even beneath the most trusted axiomatic foundations. Can you defend a proof kernel against a trusting trust attack?

import hashlib

SECRET = "no_underwear"
def validate(th):
hsh = th[0]
formula = th[1]
return hash(self.SECRET, formula) == hsh
def modus(self, th1, th2): # A => B and A gives B
if not validate(th1) or not validate(th2):
return None
match th1[1], th2[1]:
case ("=>", A,  B), A1:
if A == A1:
return (hash(self.SECRET, B), B)

# server receivers a theorem and checks it's validity

@app.route('/modus', methods=['POST'])
def modus_api():
return jsonify(modus(request.A, request.B))

if __name__ == "__main__":
app.run(debug=True, port=9999)


import requests

def send_object_to_server(obj):
response = requests.post("http://127.0.0.1:9999/send", json=obj)
return response.json()

def proof_script():

obj_to_send = {"hello": "world"}
response = send_object_to_server(obj_to_send)



## Blah Blah

Attempting to use chatgpt to write this post. Not that useful https://chat.openai.com/share/1bf6bb1a-e224-4d53-a748-b2291994fbe6

Building an interactive proof kernel is one of those things that mayb you don’t realize you can do. Like an operating system or a browser, it’s just another kind of program.

Most resources out there describe how to do this in a functional language (OCaml, Haskell, etc), but I think it’s useful to do it in a language like python. The extra impedance mismatch of bumbling through a language that is unfamiliar and a topic that is also unfamiliar can be too intimidating.

The basic approach of many proof systems is to try and build a small trusted kernel through which all your steps pass through. Then a much larger body of untrusted helper functionality can exist around this without compromising the integrity of the system.

Properties/features of the underlying language can help achieve this separation of concerns. In the LCF approach, the mechanism of abstract types is used to control valid proofs. Theorem is an abstract type, probably basically a wrapper around a formula abstract syntax tree. Just like how you can’t screw with the internals of some dictiontary data structure, you can’t screw around with the inside of Theorem. You can however request a copy of the information inside to play around with.

Python is a pretty uncontrolled language. It is hard to really really enforce any kind of discipline because there is mutability and introspection everywhere. This distinction is however a matter of degree. Every language has some kind of escape hatch. OCaml has Obj.magic and Haskell has unsafeCoerce. The point is to protect you from accidental unsoundness, not antagonistic attacks. That requires a different design.

John Harrison’s Handbook of Practical Logic and Automated Reasoning has some excellent examples of the LCF approach

# Hidden Constructor

from dataclasses import dataclass

Formula = str
Formula = set[set[int]] #cnf
Proof = list[str]

@dataclass(frozen=True)
class Theorem():
formula: Formula
proof: Proof

def resolve(t1,t2, i):
assert i in t1
assert -i in t2
return Theorem(t1.union(t2))


# Proof Objects

We so far have counted on trustability in the process. We can also just record a trace of the proof process (the sequence of calls with what parameters). We can then check this trace if we record it at a later time.

It is this method where the famed Curry Howard correspondence comes into play. These traces/proof trees can be naturally represented as lambda calculus terms for some logics.