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sol_log_reg.py
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sol_log_reg.py
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################################################
# Solution for logistic regression exercise
################################################
from pprint import pprint
import openfhe
import pandas as pd
from typing import Tuple, List
import yaml
import numpy as np
from efficient_regression.crypto_utils import create_crypto
from efficient_regression.lr_train_funcs import sol_logreg_calculate_grad, compute_loss, exe_logreg_calculate_grad
from efficient_regression.utils import next_power_of_2, collate_one_d_mat_to_ct, mat_to_ct_mat_row_major, \
one_d_mat_to_vec_col_cloned_ct, get_raw_value_from_ct, encrypt_weights
np.random.seed(42)
CT = openfhe.Ciphertext
CC = openfhe.CryptoContext
import logging
def load_data(x_file, y_file) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
Xs = pd.read_csv(x_file).to_numpy()
ys = pd.read_csv(y_file).to_numpy()
return Xs, ys, Xs, ys
def update_weights(cc: CC, ct_weights: CT, grads: CT, lr: float):
return cc.EvalSub(ct_weights, cc.EvalMult(lr, grads))
def reduce_noise(
cc,
ct_weights,
should_run_bootstrap,
num_slots_boot,
kp
):
################################################
# Exe: handle noise refreshing for both the bootstrap and iterative
# mode.
# See what happens if you forget to set the number-of-iterations in EvalBootstrap
################################################
# Bootstrapping
if should_run_bootstrap:
logger.debug(f"Bootstrapping weights for iter: {curr_epoch}")
ct_weights.SetSlots(num_slots_boot)
if openfhe.get_native_int() == "128":
ct_weights = cc.EvalBootstrap(ct_weights)
else:
ct_weights = cc.EvalBootstrap(ct_weights, 2)
else:
logger.debug(f"CT Refreshing for iter: {curr_epoch}")
_pt_weights = cc.Decrypt(
kp.secretKey,
ct_weights
)
_raw_weights = _pt_weights.GetRealPackedValue()
ct_weights = cc.Encrypt(
kp.publicKey,
cc.MakeCKKSPackedPlaintext(_raw_weights)
)
return ct_weights
if __name__ == '__main__':
with open("efficient_regression/config.yml", "r") as f:
config = yaml.safe_load(f)
logging.basicConfig(format="[%(filename)s:%(lineno)s - %(funcName)s] %(message)s",
level=getattr(logging, config["logging_level"]))
logger = logging.getLogger(__name__)
logger.debug("ML Params")
logger.debug(config["ml_params"])
logger.debug("Crypto Params")
logger.debug(config["crypto_params"])
logger.debug("Chebyshev Params")
logger.debug(config["chebyshev_params"])
if config["crypto_params"]["run_bootstrap"]:
logger.info("Running with Bootstrap")
logger.debug(config["crypto_bootstrap_params"])
ml_conf = config["ml_params"]
lr_gamma = ml_conf["lr_gamma"]
lr_eta = ml_conf["lr_eta"]
epochs = ml_conf["epochs"]
x_train, y_train, x_test, y_test = load_data(ml_conf["x_file"], ml_conf["y_file"])
original_num_samples, original_num_features = x_train.shape
beta = [[0.0] for _ in range(original_num_features)]
logger.debug("Generating crypto objects")
cc, kp, num_slots = create_crypto(
crypto_hparams=config["crypto_params"],
bootstrap_hparams=config["crypto_bootstrap_params"]
)
logger.debug("Generating crypto objects")
padded_row_size = next_power_of_2(original_num_features)
padded_col_size = num_slots / padded_row_size
# Optimization: reduces the mult depth by 1
# NOTE: we don't actually do the transpose. This is because when we use it later on
# we treat it as a col matrix, as opposed to a row matrix.
neg_x_train_T = -1 * x_train * (1 / len(x_train))
logger.debug("Generating the Sum keys")
eval_sum_row_keys = cc.EvalSumRowsKeyGen(kp.secretKey, rowSize=padded_row_size)
eval_sum_col_keys = cc.EvalSumColsKeyGen(kp.secretKey)
# Encrypt the weights
logger.debug("Generating Weights ciphertext")
ct_weights = encrypt_weights(cc, kp, beta)
logger.debug("Generating X-ciphertext")
ct_x_train = mat_to_ct_mat_row_major(
cc,
x_train.tolist(),
padded_row_size,
num_slots,
kp
)
ct_neg_x_train_T = mat_to_ct_mat_row_major(
cc,
neg_x_train_T.tolist(),
padded_row_size,
num_slots,
kp
)
logger.debug("Generating y-ciphertext")
ct_y = one_d_mat_to_vec_col_cloned_ct(
cc,
y_train.tolist(),
padded_row_size,
num_slots,
kp
)
num_features_enc = next_power_of_2(original_num_features)
num_slots_boot = num_features_enc * 8
if config["crypto_params"]["run_bootstrap"]:
logger.info("Enabling FHE features for bootstrap")
bootstrap_hparams = config["crypto_bootstrap_params"]
level_budget = bootstrap_hparams["level_budget"]
bsgs_dim = bootstrap_hparams["bsgs_dim"]
cc.Enable(openfhe.PKESchemeFeature.FHE)
cc.EvalBootstrapSetup(level_budget, bsgs_dim, num_slots_boot)
cc.EvalBootstrapKeyGen(kp.secretKey, num_slots_boot)
logger.debug("Bootstrap set up")
for curr_epoch in range(epochs):
# print(f"************************************************************\nIteration: {curr_epoch}")
if curr_epoch > 0:
ct_weights = reduce_noise(
cc=cc,
ct_weights=ct_weights,
should_run_bootstrap=config["crypto_params"]["run_bootstrap"],
num_slots_boot=num_slots_boot,
kp = kp
)
################################################
# Extract the weights
################################################
# Exe: Navigate to the exercise function for an extra difficult problem. If for time constraints you want to
# skip this (or come back to this later), comment out the first line and uncomment the second.
ct_gradient = sol_logreg_calculate_grad(
cc,
ct_x_train,
ct_neg_x_train_T,
ct_y,
ct_weights,
row_size=padded_row_size,
row_sum_keymap=eval_sum_row_keys,
col_sum_keymap=eval_sum_col_keys,
cheb_range_start=config["chebyshev_params"]["lower_bound"],
cheb_range_end=config["chebyshev_params"]["upper_bound"],
cheb_poly_degree=config["chebyshev_params"]["polynomial_degree"],
kp=kp
)
if ct_gradient is None:
raise Exception("You either "
"\ni) have not implemented exe_logreg_calculate_grad or "
"\nii) forgot to flip the function call to sol_logreg_calculate_grad")
ct_weights = update_weights(cc, ct_weights, ct_gradient, lr_eta)
if config["RUN_IN_DEBUG"]:
clear_theta = get_raw_value_from_ct(cc, ct_weights, kp, original_num_features)
loss = compute_loss(beta=clear_theta, X=x_train, y=y_train)
clear_grads = get_raw_value_from_ct(cc, ct_gradient, kp, original_num_features)
logger.info(f"Grad: {clear_grads}")
logger.info(f"Theta: {clear_theta}")
logger.info(f"Iteration: {curr_epoch} Loss: {loss}")