Tensorizing operations

This guide explains tensorization and how it can improve the execution time of Concrete circuits.

Tensors should be used instead of scalars when possible to maximize loop parallelism.

For example:

import time

import numpy as np
from concrete import fhe

inputset = fhe.inputset(fhe.uint6, fhe.uint6, fhe.uint6)
for tensorize in [False, True]:
    def f(x, y, z):
        return (
            np.sum(fhe.array([x, y, z]) ** 2)
            if tensorize
            else (x ** 2) + (y ** 2) + (z ** 2)
        )

    compiler = fhe.Compiler(f, {"x": "encrypted", "y": "encrypted", "z": "encrypted"})
    circuit = compiler.compile(inputset)

    circuit.keygen()
    for sample in inputset[:3]:  # warmup
        circuit.encrypt_run_decrypt(*sample)

    timings = []
    for sample in inputset[3:13]:
        start = time.time()
        result = circuit.encrypt_run_decrypt(*sample)
        end = time.time()

        assert np.array_equal(result, f(*sample))
        timings.append(end - start)

    if not tensorize:
        print(f"without tensorization -> {np.mean(timings):.03f}s")
    else:
        print(f"   with tensorization -> {np.mean(timings):.03f}s")

This prints:

without tensorization -> 0.214s
   with tensorization -> 0.118s

Enabling dataflow is kind of letting the runtime do this for you. It'd also help in the specific case.