def body(batch_num, mean):
if mc_sampler == tfp.mcmc.sample_halton_sequence:
start_idx = batch_num * batch_size
end_idx = start_idx + batch_size
indices = tf.range(start_idx, end_idx, dtype=tf.int32)
sample = mc_sampler(space.n_obs,
sequence_indices=indices,
dtype=ztypes.float,
randomized=False)
else:
sample = mc_sampler(shape=(batch_size, space.n_obs),
dtype=ztypes.float)
sample = tf.guarantee_const(sample)
sample = (np.array(upper[0]) -
np.array(lower[0])) * sample + lower[0]
sample = tf.transpose(a=sample)
sample = func(sample)
sample = tf.guarantee_const(sample)
batch_mean = tf.reduce_mean(input_tensor=sample)
batch_mean = tf.guarantee_const(batch_mean)
err_weight = 1 / tf.cast(batch_num + 1, dtype=tf.float64)
# err_weight /= err_weight + 1
# print_op = tf.print(batch_mean)
do_print = False
if do_print:
tf.print(batch_num + 1, mean, err_weight * (batch_mean - mean))
return batch_num + 1, mean + err_weight * (batch_mean - mean)
def body(batch_num, mean):
if mc_sampler == tfp.mcmc.sample_halton_sequence:
start_idx = batch_num * batch_size
end_idx = start_idx + batch_size
indices = tf.range(start_idx, end_idx, dtype=tf.int32)
sample = mc_sampler(space.n_obs,
sequence_indices=indices,
dtype=ztypes.float,
randomized=False)
else:
sample = mc_sampler(shape=(batch_size, space.n_obs),
dtype=ztypes.float)
sample = tf.guarantee_const(sample)
sample = (np.array(upper[0]) -
np.array(lower[0])) * sample + lower[0]
sample = tf.transpose(sample)
sample = func(sample)
sample = tf.guarantee_const(sample)
batch_mean = tf.reduce_mean(sample)
batch_mean = tf.guarantee_const(batch_mean)
# with tf.control_dependencies([batch_mean]):
err_weight = 1 / tf.to_double(batch_num + 1)
# err_weight /= err_weight + 1
# print_op = tf.print(batch_mean)
print_op = tf.print(batch_num + 1, mean,
err_weight * (batch_mean - mean))
with tf.control_dependencies([print_op]):
return batch_num + 1, mean + err_weight * (batch_mean - mean)
def MaybeGuaranteeConstGetter(getter, name, *args, **kwargs):
global _CONST_GUARANTEE
if _CONST_GUARANTEE:
with tf.control_dependencies(None):
return tf.guarantee_const(getter(name, *args, **kwargs),
name=name + '/GuaranteeConst')
else:
return getter(name, *args, **kwargs)
def __init__(self, name, value, dtype=ztypes.float):
super().__init__(name=name, params={}, dtype=dtype)
static_value = tf.get_static_value(value, partial=True)
if static_value is None:
raise RuntimeError(
"Cannot convert input to static value. If you encounter this, please open a bug report"
" on Github: https://github.com/zfit/zfit")
self._value_np = static_value
self._value = tf.guarantee_const(
tf.convert_to_tensor(value, dtype=dtype))
def __init__(self, name, value, dtype=ztypes.float):
"""
Args:
name:
value:
dtype:
"""
super().__init__(name=name, params={}, dtype=dtype)
self._value_np = tf.get_static_value(value, partial=True)
self._value = tf.guarantee_const(
tf.convert_to_tensor(value, dtype=dtype))
def __init__(self, name, K, N, L, initial_weights):
"""
Args:
K (int): The number of hidden neurons.
N (int): The number of input neurons that each hidden
neuron has.
L (int): The synaptic depth of each input neuron's weights.
"""
super(TPM, self).__init__(name=name)
self.type = 'basic'
with self.name_scope:
self.K = tf.guarantee_const(tf.constant(K, name='K'))
self.N = tf.guarantee_const(tf.constant(N, name='N'))
self.L = tf.guarantee_const(tf.constant(L, name='L'))
self.w = initial_weights
self.sigma = tf.Variable(tf.zeros([K], dtype=tf.int32),
trainable=False,
name='sigma')
self.tau = tf.Variable(0, name='tau')
self.key = tf.Variable("", name='key')
self.iv = tf.Variable("", name='iv')
def compute_key(self, key_length, iv_length):
"""Creates a key and IV based on the weights of this TPM.
Args:
key_length (int): Length of the key.
Must be 128, 192, or 256.
iv_length (int): Length of the independent variable.
Must be a multiple of 4 between 0 and 256, inclusive.
Returns:
The key and IV based on the TPM's weights.
"""
main_diagonal = tf.guarantee_const(
tf.range(tf.math.minimum(self.K, self.N)))
iv_indices = tf.guarantee_const(
tf.stack([main_diagonal, main_diagonal], axis=1))
iv_weights = tf.strings.format("{}", tf.gather_nd(self.w, iv_indices))
key_weights = tf.strings.format("{}", self.w)
def convert_to_hex_dig(input, length):
return sha512(input.numpy().decode('utf-8').encode(
'utf-8')).hexdigest()[0:length]
# TODO: figure out a way to do this without using py_function
# py_function is currently needed since we need to get the value from
# the tf.Tensor
current_key = tf.py_function(
convert_to_hex_dig, [key_weights, int(key_length / 4)],
Tout=tf.string)
current_iv = tf.py_function(
convert_to_hex_dig, [iv_weights, int(iv_length / 4)],
Tout=tf.string)
self.key.assign(current_key)
self.iv.assign(current_iv)
with self.name_scope:
tf.summary.text('key', data=current_key)
tf.summary.text('independent variable', data=current_iv)
return current_key, current_iv
def custom_getter(getter, name, *args, **kwargs):
with tf.control_dependencies(None):
return tf.guarantee_const(getter(name, *args, **kwargs),
name=name + '/GuaranteeConst')
def train_step():
# Create random vector, X, with dimensions [K, N] and values
# bound by [-1, 1]
X = tf.random.uniform(
(K, N), minval=-1, maxval=1 + 1, dtype=tf.int32)
tpm_update_rules = [
'hebbian',
'anti_hebbian',
'random_walk'
]
if update_rule == 'random-same':
# use tf.random so that the same update rule is used for
# each iteration across attacks
current_update_rule = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-A-B-E'
)
update_rule_A = current_update_rule
update_rule_B = current_update_rule
update_rule_E = current_update_rule
elif update_rule.startswith('random-different'):
update_rule_A = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-A'
)
update_rule_B = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-B'
)
if update_rule == 'random-different-A-B-E':
update_rule_E = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-E'
)
elif update_rule == 'random-different-A-B':
update_rule_E = update_rule_A
else:
raise ValueError(
f"'{update_rule}' is an invalid update rule. "
"Valid update rules are: "
"'hebbian', "
"'anti_hebbian', "
"'random_walk', "
"'random-same', "
"'random-different-A-B', and "
"'random-different-A-B-E'"
)
# elif tf.reduce_any(tf.math.equal(update_rule, tpm_update_rules)):
elif not isinstance(tpm_update_rules, tf.Tensor) and \
update_rule in tpm_update_rules:
current_update_rule = tf.guarantee_const(update_rule)
update_rule_A = current_update_rule
update_rule_B = current_update_rule
update_rule_E = current_update_rule
else:
raise ValueError(
f"'{update_rule}' is an invalid update rule. "
"Valid update rules are: "
"'hebbian', "
"'anti_hebbian', "
"'random_walk', "
"'random-same', "
"'random-different-A-B', and "
"'random-different-A-B-E'"
)
iterate(
X,
Alice, Bob, Eve,
update_rule_A, update_rule_B, update_rule_E,
nb_updates, nb_eve_updates,
score, score_eve,
key_length, iv_length
)
tf.summary.experimental.set_step(nb_updates)
def run(
update_rule, K, N, L,
attack,
initial_weights,
key_length=256, iv_length=128
):
with tf.experimental.async_scope():
tf.print(
"\n\n\n",
"Creating machines: K=", K, ", N=", N, ", L=", L, ", ",
"update-rule=", update_rule, ", ",
"attack=", attack,
"\n",
sep='',
name='log-run-initialization'
)
Alice = TPM('Alice', K, N, L, initial_weights['Alice'])
Bob = TPM('Bob', K, N, L, initial_weights['Bob'])
if attack == 'probabilistic':
from mlencrypt_research.tpms import ProbabilisticTPM
Eve = ProbabilisticTPM('Eve', K, N, L)
elif attack == 'geometric':
from mlencrypt_research.tpms import GeometricTPM
Eve = GeometricTPM('Eve', K, N, L, initial_weights['Eve'])
elif attack == 'none':
Eve = TPM('Eve', K, N, L, initial_weights['Eve'])
else:
# TODO: better message for ValueError
raise ValueError
# try:
# synchronization score of Alice and Bob
score = tf.Variable(0., trainable=False,
name='score-A-B', dtype=tf.float32)
# synchronization score of Alice and Eve
score_eve = tf.Variable(0., trainable=False,
name='score-A-E', dtype=tf.float32)
# except ValueError:
# # tf.function-decorated function tried to create variables
# # on non-first call.
# score = 0.
# score_eve = 0.
# https://www.tensorflow.org/tutorials/customization/performance#zero_iterations
with Alice.name_scope:
Alice.key = tf.Variable("", trainable=False, name='key')
Alice.iv = tf.Variable("", trainable=False, name='iv')
with Bob.name_scope:
Bob.key = tf.Variable("", trainable=False, name='key')
Bob.iv = tf.Variable("", trainable=False, name='iv')
with Eve.name_scope:
Eve.key = tf.Variable("", trainable=False, name='key')
Eve.iv = tf.Variable("", trainable=False, name='iv')
# try:
nb_updates = tf.Variable(
0, name='updates-A-B', trainable=False, dtype=tf.int64)
nb_eve_updates = tf.Variable(
0, name='updates-E', trainable=False, dtype=tf.int64)
# except ValueError:
# # tf.function-decorated function tried to create variables
# # on non-first call.
# pass
autograph_features = tf.autograph.experimental.Feature.all_but([
tf.autograph.experimental.Feature.AUTO_CONTROL_DEPS,
tf.autograph.experimental.Feature.NAME_SCOPES,
])
@tf.function(
experimental_autograph_options=autograph_features,
experimental_relax_shapes=True,
)
def train_step():
# Create random vector, X, with dimensions [K, N] and values
# bound by [-1, 1]
X = tf.random.uniform(
(K, N), minval=-1, maxval=1 + 1, dtype=tf.int32)
tpm_update_rules = [
'hebbian',
'anti_hebbian',
'random_walk'
]
if update_rule == 'random-same':
# use tf.random so that the same update rule is used for
# each iteration across attacks
current_update_rule = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-A-B-E'
)
update_rule_A = current_update_rule
update_rule_B = current_update_rule
update_rule_E = current_update_rule
elif update_rule.startswith('random-different'):
update_rule_A = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-A'
)
update_rule_B = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-B'
)
if update_rule == 'random-different-A-B-E':
update_rule_E = select_random_from_list(
tpm_update_rules,
op_name='iteration-ur-E'
)
elif update_rule == 'random-different-A-B':
update_rule_E = update_rule_A
else:
raise ValueError(
f"'{update_rule}' is an invalid update rule. "
"Valid update rules are: "
"'hebbian', "
"'anti_hebbian', "
"'random_walk', "
"'random-same', "
"'random-different-A-B', and "
"'random-different-A-B-E'"
)
# elif tf.reduce_any(tf.math.equal(update_rule, tpm_update_rules)):
elif not isinstance(tpm_update_rules, tf.Tensor) and \
update_rule in tpm_update_rules:
current_update_rule = tf.guarantee_const(update_rule)
update_rule_A = current_update_rule
update_rule_B = current_update_rule
update_rule_E = current_update_rule
else:
raise ValueError(
f"'{update_rule}' is an invalid update rule. "
"Valid update rules are: "
"'hebbian', "
"'anti_hebbian', "
"'random_walk', "
"'random-same', "
"'random-different-A-B', and "
"'random-different-A-B-E'"
)
iterate(
X,
Alice, Bob, Eve,
update_rule_A, update_rule_B, update_rule_E,
nb_updates, nb_eve_updates,
score, score_eve,
key_length, iv_length
)
tf.summary.experimental.set_step(nb_updates)
# In the long run, perf_counter doesn't help because different CPUs
# have different performance. For the sake of prosperity, we use the
# number of training steps, although training time would be more useful
# in a practical setting.
# start_time = perf_counter()
while not tf.reduce_all(tf.math.equal(Alice.w, Bob.w)):
# TODO: instead of while, use for until L^4*K*N and break
train_step()
# end_time = perf_counter()
# training_time = end_time - start_time
# loss = (tf.math.sigmoid(training_time) + score_eve / 100.) / 2.
# for reference, log(120) = 2.08 and log(43000) = 4.63
loss = tf.math.log(tf.cast(nb_updates, tf.float32)) * score_eve / 100.
key_length = tf.guarantee_const(tf.constant(key_length))
iv_length = tf.guarantee_const(tf.constant(iv_length))
if getenv('MLENCRYPT_BARE', 'FALSE') == 'TRUE':
# because score_eve hasn't been calculated yet in bare mode
if attack == 'probabilistic':
eve_w = Eve.mpW
else:
eve_w = Eve.w
score_eve.assign(
100. * sync_score(Alice.w, eve_w, Alice.name, Eve.name),
name='calc-sync-A-E'
)
else:
if getenv('MLENCRYPT_TB', 'FALSE') == 'TRUE':
tf.print(
"\n\n",
"Alice's key: ", Alice.key, " iv: ", Alice.iv, "\n",
"Bob's key: ", Bob.key, " iv: ", Bob.iv, "\n",
"Eve's key: ", Eve.key, " iv: ", Eve.iv,
sep='',
name='log-run-final'
)
if tf.math.equal(getenv('MLENCRYPT_TB', 'FALSE'), 'TRUE', name='log-tb'):
# create scatterplots (in scalars dashboard) of metric vs steps
# tf.summary.scalar('training_time', training_time)
tf.summary.scalar('eve_score', score_eve)
tf.summary.scalar('loss', loss)
return nb_updates, score_eve, loss
def sync_score(tpm1_w, tpm2_w, tpm1_name, tpm2_name):
"""
Args:
TPM1: Tree Parity Machine 1. Has same parameters as TPM2.
TPM2: Tree Parity Machine 2. Has same parameters as TPM1.
Returns:
The synchronization score between TPM1 and TPM2.
"""
tpm1_id, tpm2_id = tpm1_name[0], tpm2_name[0]
# adapted from:
# https://github.com/tensorflow/tensorflow/blob/r2.2/tensorflow/python/keras/losses.py#L1672-L1716
# TODO: am I using experimental_implements correctly?
@tf.function(experimental_implements="cosine_similarity")
def cosine_similarity(weights1, weights2):
"""Computes the cosine similarity between labels and predictions.
Note that it is a negative quantity between 0 and 1, where 0 indicates
orthogonality and values closer to 1 indicate greater similarity.
`loss = sum(y_true * y_pred)`
Args:
y_true: Tensor of true targets.
y_pred: Tensor of predicted targets.
axis: Axis along which to determine similarity.
Returns:
Cosine similarity tensor.
"""
weights1_float = tf.cast(weights1, tf.float32,
name=f'weights-{tpm1_id}-float')
weights2_float = tf.cast(weights2, tf.float32,
name=f'weights-{tpm2_id}-float')
weights1_norm = tf.math.l2_normalize(weights1_float)
weights2_norm = tf.math.l2_normalize(weights2_float)
# this doesn't work well; cos_sim is occasionally greater than 1.
# this is also marginally slower:
# cos_sim = tf.tensordot(weights1_norm, weights2_norm,
# [[0, 1], [0, 1]])
# return cos_sim
# cos_sim is bound by [-1, 1] (for the most part); note that with
# cos_sim is still occasionally greater than 1:
cos_sim = -tf.math.reduce_sum(weights1_norm * weights2_norm)
# we change cos_sim's range to [0, 1] according to:
# https://arxiv.org/pdf/0711.2411.pdf#page=62
return -cos_sim / 2. + .5
rho = cosine_similarity(tpm1_w, tpm2_w)
if getenv('MLENCRYPT_TB', 'FALSE') == 'TRUE':
# the generalization error, epsilon, is the probability that a
# repulsive step occurs if two corresponding hidden units have
# different sigma (Ruttor, 2006).
epsilon = tf.math.multiply(
tf.guarantee_const(tf.constant(1. / pi), name='reciprocal-pi'),
tf.bitcast(
tf.math.acos(rho, name=f'angle-{tpm1_id}-{tpm2_id}'),
tf.float32,
name=f'angle-{tpm1_id}-{tpm2_id}'
),
name=f'scale-angle-{tpm1_id}-{tpm2_id}-to-0-1'
)
with tf.name_scope(f'{tpm1_name}-{tpm2_name}'):
tf.summary.scalar('sync', data=rho)
tf.summary.scalar('generalization-error', data=epsilon)
return rho
def dummy_func(fake_x): # to make working with custom_gradient
if x is not None:
raise DueToLazynessNotImplementedError(
"partial not yet implemented")
def body(batch_num, mean):
if mc_sampler == tfp.mcmc.sample_halton_sequence:
start_idx = batch_num * batch_size
end_idx = start_idx + batch_size
indices = tf.range(start_idx, end_idx, dtype=tf.int32)
sample = mc_sampler(space.n_obs,
sequence_indices=indices,
dtype=ztypes.float,
randomized=False)
else:
sample = mc_sampler(shape=(batch_size, space.n_obs),
dtype=ztypes.float)
sample = tf.guarantee_const(sample)
sample = (np.array(upper[0]) -
np.array(lower[0])) * sample + lower[0]
sample = tf.transpose(sample)
sample = func(sample)
sample = tf.guarantee_const(sample)
batch_mean = tf.reduce_mean(sample)
batch_mean = tf.guarantee_const(batch_mean)
# with tf.control_dependencies([batch_mean]):
err_weight = 1 / tf.to_double(batch_num + 1)
# err_weight /= err_weight + 1
# print_op = tf.print(batch_mean)
print_op = tf.print(batch_num + 1, mean,
err_weight * (batch_mean - mean))
with tf.control_dependencies([print_op]):
return batch_num + 1, mean + err_weight * (batch_mean - mean)
# return batch_num + 1, tf.guarantee_const(mean + err_weight * (batch_mean - mean))
cond = lambda batch_num, _: batch_num < num_batches
initial_mean = tf.convert_to_tensor(0, dtype=ztypes.float)
_, final_mean = tf.while_loop(cond,
body, (0, initial_mean),
parallel_iterations=1,
swap_memory=False,
back_prop=False,
maximum_iterations=num_batches)
def dummy_grad_with_var(dy, variables=None):
raise DueToLazynessNotImplementedError("Who called me? Mayou36")
if variables is None:
raise DueToLazynessNotImplementedError(
"Is this needed? Why? It's not a NN. Please make an issue."
)
def dummy_grad_func(x):
values = func(x)
if variables:
gradients = tf.gradients(values, variables, grad_ys=dy)
else:
gradients = None
return gradients
return chunked_average(func=dummy_grad_func,
x=x,
num_batches=num_batches,
batch_size=batch_size,
space=space,
mc_sampler=mc_sampler)
def dummy_grad_without_var(dy):
return dummy_grad_with_var(dy=dy, variables=None)
print_op = tf.print(final_mean)
with tf.control_dependencies([print_op]):
return tf.guarantee_const(final_mean), dummy_grad_with_var
