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Differentiable IR for Zero-Knowledge Machine Learning

Project description

yarrow-polycirc: Differentiable IR for Zero-Knowledge Machine Learning

Documentation Status

yarrow-polycirc is a a library for representing and differentiating polynomial (arithmetic) circuits.

It's used for two things:

  1. A graph-based, platform-agnostic datastructure for representing arithmetic circuits
  2. Differentiating arithmetic circuits for verified Zero-Knowledge ML.

Install with pip:

pip install yarrow-polycirc

Polynomial Circuits

Polynomial circuits are a bit like boolean circuits, except their wires can have values in an arbitrary semiring, not just {0, 1}.

Here's an example of a polynomial circuit over the semiring Z_32:

x₀ ───────────┐   ┌───┐
              └───┤   │
                  │ * ├──── y₀
      ┌───┐   ┌───┤   │
x₁ ───┤ - ├───┘   └───┘

Think of this circuit as computing the expression y₀ = x₀ * (-x₁). The "dangling wires" on the left are the inputs x₀ and x₁, and on the right are the outputs y₀.


Polycirc provides a function rdiff to differentiate circuits:

from polycirc import ir, rdiff
c = # example circuit - dot product of two 2-dimensional vectors
rc = rdiff(c)

See ./examples/ for an example of using rdiff for machine learning. Theoretical details on rdiff are in Section 10 of this paper.

How to use it

Use the ir module to build circuits in an algebraic style. For example, let's first import the ir module:

from polycirc import ir

Basic circuits in the IR can be combined by stacking:

f = ir.identity(1) @ ir.negate(1)

Think of f visually like this:

x₀ ─────────── y₀
x₁ ───┤ - ├─── y₁

We can plug the outputs of f into another circuit using the composition operation >>:

c = f >> ir.mul(1)

which looks like this:

x₀ ───────────┐   ┌───┐
              └───┤   │
                  │ * ├──── y₀
      ┌───┐   ┌───┤   │
x₁ ───┤ - ├───┘   └───┘

We can print this program as Python code:

from polycirc.ast import diagram_to_ast
print(diagram_to_ast(c, 'multiply_negate'))

And we get the following:

def multiply_negate(x0, x1):
    x2 = - (x1)
    x3 = x0 * x2
    return [x3]

Iris demo

See the included iris example which uses differentiability of the IR to train a simple linear model for the iris dataset. Both training and inference of this model happens with polynomial circuits, so yarrow-polycirc can be used for on-chain training of models.

First, install the example dependencies

pip install '.[dev,example]'

Download the Iris dataset


Run the example:

python -m examples.iris train

You should see something like this:

loading data...
final parameters [-118, 912, -884, 408, -20, -189, 68, 28, -1407, -641, 1407, 2336]
accuracy: 96.0

You can also print the iris model circuits as python code:

> python -m examples.iris print
def predict(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x17, x22, x27):
    x13 = x12
    x14 = x12
    x18 = x17


Adding a backend

If you want to add your own backend, you don't need to work with diagrams (circuits) directly. Instead, you can use the polycirc.ast module to transform a circuit to a generic AST with polycirc.ast.diagram_to_ast.

from polycirc.ast import diagram_to_ast
c = make_some_circuit()
diagram_to_ast(c, 'function_name')

From this generic AST, you can then convert your arithmetic circuit to your target language.

For an in depth example, see the provided Leo backend example and the docs

Running Tests

Install dev dependencies

pip install '.[dev]'

and run tests


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