def test_cnn_mlp_match_sliding_window(self):
num_steps = 1000
for seed in range(1):
verif_instance = test_utils.make_toy_verif_instance(seed,
nn='cnn_slide')
key = jax.random.PRNGKey(0)
lb, ub, params_mlp = test_utils.make_mlp_verif_instance_from_cnn(
verif_instance)
bounds_mlp = sdp_verify.boundprop(
params_mlp,
sdp_verify.IntBound(lb=lb, ub=ub, lb_pre=None, ub_pre=None))
verif_instance_mlp = utils.make_nn_verif_instance(
params_mlp, bounds_mlp)
dual_ub_cnn, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance),
key,
num_steps=num_steps,
verbose=False,
use_exact_eig_train=True)
dual_ub_mlp, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance_mlp),
key,
num_steps=num_steps,
verbose=False,
use_exact_eig_train=True)
assert abs(dual_ub_cnn - dual_ub_mlp) < 5e-3, (
'Dual upper bound for MLP and CNN (sliding filter) should match.'
f'Seed is {seed}. Vals are CNN: {dual_ub_cnn} MLP: {dual_ub_mlp}'
)
def test_cnn_mlp_match_fixed_window(self):
num_steps = 1000
for seed in range(1):
verif_instance = test_utils.make_toy_verif_instance(seed, nn='cnn_simple')
key = jax.random.PRNGKey(0)
params_cnn = verif_instance.params_full
in_shape = int(np.prod(np.array(params_cnn[0]['W'].shape[:-1])))
# Input and filter size match -> filter is applied at just one location.
# Number of layer 1 neurons = no. channels out of conv filter (last dim).
out_shape = params_cnn[0]['W'].shape[-1]
params_mlp = [(jnp.reshape(params_cnn[0]['W'],
(in_shape, out_shape)), params_cnn[0]['b']),
(params_cnn[1][0], params_cnn[1][1])]
bounds_mlp = sdp_verify.boundprop(
params_mlp,
sdp_verify.IntBound(
lb=np.zeros((1, in_shape)),
ub=1 * np.ones((1, in_shape)),
lb_pre=None,
ub_pre=None))
verif_instance_mlp = utils.make_nn_verif_instance(params_mlp, bounds_mlp)
dual_ub_cnn, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key,
num_steps=num_steps, verbose=False, use_exact_eig_train=True)
dual_ub_mlp, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance_mlp), key,
num_steps=num_steps, verbose=False, use_exact_eig_train=True)
assert abs(dual_ub_cnn - dual_ub_mlp) < 1e-2, (
'Dual upper bound for MLP and CNN (simple CNN) should match.'
f'Seed is {seed}. Vals are CNN: {dual_ub_cnn} MLP: {dual_ub_mlp}')
def test_sdp_dual_simple_no_crash(self, model_type):
verif_instance = test_utils.make_toy_verif_instance(seed=0,
target_label=1,
label=2,
nn=model_type)
kwargs = {
'key': jax.random.PRNGKey(0),
'opt': optax.adam(1e-3),
'num_steps': 10,
'eval_every': 5,
'verbose': False,
'use_exact_eig_eval': False,
'use_exact_eig_train': False,
'n_iter_lanczos': 5,
'kappa_reg_weight': 1e-5,
'kappa_zero_after': 8,
'device_type': None,
}
verif_instance = utils.make_sdp_verif_instance(verif_instance)
# Check all kwargs work.
dual_val, _ = sdp_verify.solve_sdp_dual_simple(verif_instance,
**kwargs)
assert isinstance(dual_val, float)
# Check code runs without kwargs.
dual_val, _ = sdp_verify.solve_sdp_dual_simple(verif_instance,
num_steps=5)
assert isinstance(dual_val, float)
def test_dual_sdp_no_crash(self):
for nn in ['cnn', 'mlp']:
verif_instance = test_utils.make_toy_verif_instance(
seed=0, target_label=1, label=2, nn=nn)
key = jax.random.PRNGKey(0)
dual_val, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key, num_steps=10,
n_iter_lanczos=5)
assert isinstance(dual_val, float)
def test_crossing_bounds(self):
loss_margin = 1e-3
seed = random.randint(1, 10000)
verif_instance = test_utils.make_toy_verif_instance(seed)
key = jax.random.PRNGKey(0)
primal_opt, _ = cvxpy_verify.solve_mip_mlp_elided(verif_instance)
dual_ub, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key, num_steps=1000)
assert dual_ub > primal_opt - loss_margin, (
'Dual upper bound should be greater than optimal primal objective.'
f'Seed is {seed}. Vals are Dual: {dual_ub} Primal: {primal_opt}')
def test_correct_dual_var_types(self):
for nn in ['cnn', 'mlp']:
verif_instance = test_utils.make_toy_verif_instance(
seed=0, target_label=1, label=2, nn=nn)
key = jax.random.PRNGKey(0)
dual_vars = sdp_verify.init_duals(
utils.make_sdp_verif_instance(verif_instance), key)
assert len(dual_vars) == 3, 'Input, one hidden layer, kappa'
assert isinstance(dual_vars[0], sdp_verify.DualVar)
assert isinstance(dual_vars[1], sdp_verify.DualVarFin)
assert isinstance(dual_vars[2], jax.interpreters.xla.DeviceArray)
def _test_tight_duality_gap(self, seed, loss_margin=0.003, num_steps=3000):
verif_instance = test_utils.make_toy_verif_instance(
seed, label=1, target_label=2)
key = jax.random.PRNGKey(0)
primal_opt, _ = cvxpy_verify.solve_sdp_mlp_elided(verif_instance)
dual_ub, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key, num_steps=num_steps,
verbose=False)
assert dual_ub - primal_opt < loss_margin, (
'Primal and dual vals should be close. '
f'Seed: {seed}. Primal: {primal_opt}, Dual: {dual_ub}')
assert dual_ub > primal_opt - 1e-3, 'crossing bounds'
def test_ibp_init_matches_ibp_bound(self):
for nn in ['cnn', 'mlp']:
for seed in range(20):
orig_verif_instance = test_utils.make_toy_verif_instance(seed, nn=nn)
key = jax.random.PRNGKey(0)
verif_instance = utils.make_sdp_verif_instance(orig_verif_instance)
dual_vars = jax.tree_map(lambda s: None if s is None else jnp.zeros(s),
verif_instance.dual_shapes)
dual_vars = sdp_verify.init_duals_ibp(verif_instance, dual_vars)
dual_loss = sdp_verify.dual_fun(
verif_instance, dual_vars, key, exact=True)
ibp_bound = utils.ibp_bound_elided(orig_verif_instance)
assert abs(dual_loss - ibp_bound) < 1e-4, (
f'Loss at initialization should match IBP: {dual_loss} {ibp_bound}')
def verify_cnn_single_dual(verif_instance):
"""Run verification for a CNN on a single MNIST/CIFAR problem."""
verif_instance = utils.make_sdp_verif_instance(verif_instance)
solver_params = dict(
use_exact_eig_train=FLAGS.use_exact_eig_train,
use_exact_eig_eval=FLAGS.use_exact_eig_eval,
n_iter_lanczos=FLAGS.n_iter_lanczos,
eval_every=FLAGS.eval_every,
opt_name=FLAGS.opt_name,
anneal_factor=FLAGS.anneal_factor,
lr_init=FLAGS.lr_init,
kappa_zero_after=FLAGS.kappa_zero_after,
kappa_reg_weight=FLAGS.kappa_reg_weight,
)
# Set schedule
steps_per_anneal = [int(x) for x in FLAGS.anneal_lengths.split(',')]
num_steps = sum(steps_per_anneal)
solver_params['steps_per_anneal'] = steps_per_anneal[:-1] + [int(1e9)]
# Set learning rate multipliers
kappa_shape = verif_instance.dual_shapes[-1]
kappa_index = len(verif_instance.dual_shapes) - 1
assert len(kappa_shape) == 2 and kappa_shape[0] == 1
opt_multiplier_fn = functools.partial(_opt_multiplier_fn,
kappa_index=kappa_index,
kappa_dim=kappa_shape[1])
# Call solver
obj_value, info = sdp_verify.solve_sdp_dual(
verif_instance,
num_steps=num_steps,
verbose=True,
opt_multiplier_fn=opt_multiplier_fn,
**solver_params)
info['final_dual_vars'] = jax.tree_map(np.array, info['final_dual_vars'])
return float(obj_value), info