class ExperimentalOptimizersEquivalenceTest(chex.TestCase):
def setUp(self):
super().setUp()
self.init_params = (jnp.array([1., 2.]), jnp.array([3., 4.]))
self.per_step_updates = (jnp.array([500., 5.]), jnp.array([300., 3.]))
@chex.all_variants()
@parameterized.named_parameters(
('sgd', alias.sgd(LR, 0.0), optimizers.sgd(LR), 1e-5),
('adam', alias.adam(LR, 0.9, 0.999,
1e-8), optimizers.adam(LR, 0.9, 0.999), 1e-4),
('rmsprop', alias.rmsprop(
LR, decay=.9, eps=0.1), optimizers.rmsprop(LR, .9, 0.1), 1e-5),
('rmsprop_momentum', alias.rmsprop(LR, decay=.9, eps=0.1,
momentum=0.9),
optimizers.rmsprop_momentum(LR, .9, 0.1, 0.9), 1e-5),
('adagrad', alias.adagrad(
LR,
0.,
0.,
), optimizers.adagrad(LR, 0.), 1e-5),
('sgd', alias.sgd(LR_SCHED, 0.0), optimizers.sgd(LR), 1e-5),
('adam', alias.adam(LR_SCHED, 0.9, 0.999,
1e-8), optimizers.adam(LR, 0.9, 0.999), 1e-4),
('rmsprop', alias.rmsprop(LR_SCHED, decay=.9, eps=0.1),
optimizers.rmsprop(LR, .9, 0.1), 1e-5),
('rmsprop_momentum',
alias.rmsprop(LR_SCHED, decay=.9, eps=0.1, momentum=0.9),
optimizers.rmsprop_momentum(LR, .9, 0.1, 0.9), 1e-5),
('adagrad', alias.adagrad(
LR_SCHED,
0.,
0.,
), optimizers.adagrad(LR, 0.), 1e-5),
)
def test_jax_optimizer_equivalent(self, optax_optimizer, jax_optimizer,
rtol):
# experimental/optimizers.py
jax_params = self.init_params
opt_init, opt_update, get_params = jax_optimizer
state = opt_init(jax_params)
for i in range(STEPS):
state = opt_update(i, self.per_step_updates, state)
jax_params = get_params(state)
# optax
optax_params = self.init_params
state = optax_optimizer.init(optax_params)
@self.variant
def step(updates, state):
return optax_optimizer.update(updates, state)
for _ in range(STEPS):
updates, state = step(self.per_step_updates, state)
optax_params = update.apply_updates(optax_params, updates)
# Check equivalence.
chex.assert_tree_all_close(jax_params, optax_params, rtol=rtol)
def args_to_op(optimizer_string, lr, mom=0.9, var=0.999, eps=1e-7):
return {
"gd": lambda lr, *unused: op.sgd(lr),
"sgd": lambda lr, *unused: op.sgd(lr),
"momentum": lambda lr, mom, *unused: op.momentum(lr, mom),
"adam": lambda lr, mom, var, eps: op.adam(lr, mom, var, eps),
}[optimizer_string.lower()](lr, mom, var, eps)
def _define_optimizer_JAX(dataset, learn_rate):
if 'purchases' == dataset:
return optimizers.adam(learn_rate)
# return optimizers.sgd(learn_rate) # temporarily error....
elif 'fashion_mnist' == dataset:
return optimizers.sgd(learn_rate)
elif 'cifar10' == dataset:
return optimizers.sgd(learn_rate)
else:
assert False, ('Error: unknown dataset - {}'.format(dataset))
def train_devise(
weights,
X,
y,
label_embeddings,
margin,
n_epochs=2,
learning_rate=0.001,
batch_size=16,
):
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
opt_state = opt_init(weights)
loader = torch_util.get_dataloader(np.array(X),
np.array(y),
batch_size=batch_size)
loss_fn = get_similarity_based_hinge_loss(label_embeddings, X, y, margin)
gradient_fn = jax.jit(jax.grad(loss_fn))
for i in tqdm.tqdm(range(n_epochs)):
for (j, (embeddings_np, labels_np)) in enumerate(loader):
embeddings, labels = jnp.array(embeddings_np), jnp.array(labels_np)
grads = gradient_fn(weights)
opt_state = opt_update(j, grads, opt_state)
weights = get_params(opt_state)
return weights
def fit_mixture(data,
num_components=3,
verbose=False,
num_samples=5000) -> LogisticMixtureParams:
# the data might be something weird, like a pandas dataframe column;
# turn it into a regular old numpy array
data_as_np_array = np.array(data)
step_size = 0.01
components = initialize_components(num_components)
(init_fun, update_fun, get_params) = sgd(step_size)
opt_state = init_fun(components)
for i in tqdm(range(num_samples)):
components = get_params(opt_state)
grads = -grad_mixture_logpdf(data_as_np_array, components)
if np.any(np.isnan(grads)):
print("Encoutered nan gradient, stopping early")
print(grads)
print(components)
break
grads = clip_grads(grads, 1.0)
opt_state = update_fun(i, grads, opt_state)
if i % 500 == 0 and verbose:
pprint(components)
score = mixture_logpdf(data_as_np_array, components)
print(f"Log score: {score:.3f}")
return structure_mixture_params(components)
def test_optimize_rotation(self):
opt_init, opt_update, get_params = optimizers.sgd(5e-2)
x0 = onp.array([
[1.0, 0.2, 3.3], # H
[-0.6,-1.1,-0.9],# C
[3.4, 5.5, 0.2], # H
[3.6, 5.6, 0.6], # H
], dtype=onp.float64)
x1 = onp.array([
[1.0, 0.2, 3.3], # H
[-0.6,-1.1,-0.9],# C
[3.4, 5.5, 0.2], # H
[3.6, 5.6, 0.6], # H
], dtype=onp.float64) + onp.random.rand(x0.shape[0],3)*10
grad_fn = jax.jit(jax.grad(rmsd.opt_rot_rmsd, argnums=(0,)))
opt_state = opt_init(x0)
for i in range(1500):
g = grad_fn(get_params(opt_state), x1)[0]
opt_state = opt_update(i, g, opt_state)
x_final = get_params(opt_state)
assert rmsd.opt_rot_rmsd(x_final, x1) < 0.1
def testMaxLearningRate(self, train_shape, network, out_logits,
fn_and_kernel):
key = stateless_uniform(shape=[2],
seed=[0, 0],
minval=None,
maxval=None,
dtype=tf.int32)
keys = tf_random_split(key)
key = keys[0]
split = keys[1]
if len(train_shape) == 2:
train_shape = (train_shape[0] * 5, train_shape[1] * 10)
else:
train_shape = (16, 8, 8, 3)
x_train = np.asarray(normal(train_shape, seed=split))
keys = tf_random_split(key)
key = keys[0]
split = keys[1]
y_train = np.asarray(
stateless_uniform(shape=(train_shape[0], out_logits),
seed=split,
minval=0,
maxval=1) < 0.5, np.float32)
# Regress to an MSE loss.
loss = lambda params, x: 0.5 * np.mean((f(params, x) - y_train)**2)
grad_loss = jit(grad(loss))
def get_loss(opt_state):
return loss(get_params(opt_state), x_train)
steps = 20
for lr_factor in [0.5, 3.]:
params, f, ntk = fn_and_kernel(key, train_shape[1:], network,
out_logits)
g_dd = ntk(x_train, None, 'ntk')
step_size = predict.max_learning_rate(
g_dd, y_train_size=y_train.size) * lr_factor
opt_init, opt_update, get_params = optimizers.sgd(step_size)
opt_state = opt_init(params)
init_loss = get_loss(opt_state)
for i in range(steps):
params = get_params(opt_state)
opt_state = opt_update(i, grad_loss(params, x_train),
opt_state)
trained_loss = get_loss(opt_state)
loss_ratio = trained_loss / (init_loss + 1e-12)
if lr_factor == 3.:
if not math.isnan(loss_ratio):
self.assertGreater(loss_ratio, 10.)
else:
self.assertLess(loss_ratio, 0.1)
def testNTKGDPrediction(self, train_shape, test_shape, network, out_logits,
fn_and_kernel, momentum, learning_rate, t, loss):
key, x_test, x_train, y_train = self._get_inputs(out_logits, test_shape,
train_shape)
params, f, ntk = fn_and_kernel(key, train_shape[1:], network, out_logits)
g_dd = ntk(x_train, None, 'ntk')
g_td = ntk(x_test, x_train, 'ntk')
# Regress to an MSE loss.
loss_fn = lambda y, y_hat: 0.5 * np.mean((y - y_hat)**2)
grad_loss = jit(grad(lambda params, x: loss_fn(f(params, x), y_train)))
trace_axes = () if g_dd.ndim == 4 else (-1,)
if loss == 'mse_analytic':
if momentum is not None:
raise absltest.SkipTest(momentum)
predictor = predict.gradient_descent_mse(g_dd, y_train,
learning_rate=learning_rate,
trace_axes=trace_axes)
elif loss == 'mse':
predictor = predict.gradient_descent(loss_fn, g_dd, y_train,
learning_rate=learning_rate,
momentum=momentum,
trace_axes=trace_axes)
else:
raise NotImplementedError(loss)
predictor = jit(predictor)
fx_train_0 = f(params, x_train)
fx_test_0 = f(params, x_test)
self._test_zero_time(predictor, fx_train_0, fx_test_0, g_td, momentum)
self._test_multi_step(predictor, fx_train_0, fx_test_0, g_td, momentum)
if loss == 'mse_analytic':
self._test_inf_time(predictor, fx_train_0, fx_test_0, g_td, y_train)
if momentum is None:
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
else:
opt_init, opt_update, get_params = optimizers.momentum(learning_rate,
momentum)
opt_state = opt_init(params)
for i in range(t):
params = get_params(opt_state)
opt_state = opt_update(i, grad_loss(params, x_train), opt_state)
params = get_params(opt_state)
fx_train_nn, fx_test_nn = f(params, x_train), f(params, x_test)
fx_train_t, fx_test_t = predictor(t, fx_train_0, fx_test_0, g_td)
self.assertAllClose(fx_train_nn, fx_train_t, rtol=RTOL, atol=ATOL)
self.assertAllClose(fx_test_nn, fx_test_t, rtol=RTOL, atol=ATOL)
def testIssue758(self):
# code from https://github.com/google/jax/issues/758
# this is more of a scan + jacfwd/jacrev test, but it lives here to use the
# optimizers.py code
def harmonic_bond(conf, params):
return np.sum(conf * params)
opt_init, opt_update, get_params = optimizers.sgd(5e-2)
x0 = onp.array([0.5], dtype=onp.float64)
params = onp.array([0.3], dtype=onp.float64)
def minimize_structure(test_params):
energy_fn = functools.partial(harmonic_bond, params=test_params)
grad_fn = grad(energy_fn, argnums=(0, ))
opt_state = opt_init(x0)
def apply_carry(carry, _):
i, x = carry
g = grad_fn(get_params(x))[0]
new_state = opt_update(i, g, x)
new_carry = (i + 1, new_state)
return new_carry, _
carry_final, _ = lax.scan(apply_carry, (0, opt_state),
np.zeros((75, 0)))
trip, opt_final = carry_final
assert trip == 75
return opt_final
initial_params = np.float64(0.5)
minimize_structure(initial_params)
def loss(test_params):
opt_final = minimize_structure(test_params)
return 1.0 - get_params(opt_final)[0]
loss_opt_init, loss_opt_update, loss_get_params = optimizers.sgd(5e-2)
J1 = jacrev(loss, argnums=(0, ))(initial_params)
J2 = jacfwd(loss, argnums=(0, ))(initial_params)
self.assertAllClose(J1, J2, check_dtypes=True, rtol=1e-6)
def testMaxLearningRate(self, train_shape, network, out_logits,
fn_and_kernel, name):
key = random.PRNGKey(0)
key, split = random.split(key)
if len(train_shape) == 2:
train_shape = (train_shape[0] * 5, train_shape[1] * 10)
else:
train_shape = (16, 8, 8, 3)
x_train = random.normal(split, train_shape)
key, split = random.split(key)
y_train = np.array(
random.bernoulli(split, shape=(train_shape[0], out_logits)),
np.float32)
for lr_factor in [0.5, 3.]:
params, f, ntk = fn_and_kernel(key, train_shape[1:], network,
out_logits)
# Regress to an MSE loss.
loss = lambda params, x: \
0.5 * np.mean((f(params, x) - y_train) ** 2)
grad_loss = jit(grad(loss))
g_dd = ntk(x_train, None, 'ntk')
steps = 20
if name == 'theoretical':
step_size = predict.max_learning_rate(
g_dd, num_outputs=out_logits) * lr_factor
else:
step_size = predict.max_learning_rate(
g_dd, num_outputs=-1) * lr_factor
opt_init, opt_update, get_params = optimizers.sgd(step_size)
opt_state = opt_init(params)
def get_loss(opt_state):
return loss(get_params(opt_state), x_train)
init_loss = get_loss(opt_state)
for i in range(steps):
params = get_params(opt_state)
opt_state = opt_update(i, grad_loss(params, x_train),
opt_state)
trained_loss = get_loss(opt_state)
loss_ratio = trained_loss / (init_loss + 1e-12)
if lr_factor == 3.:
if not math.isnan(loss_ratio):
self.assertGreater(loss_ratio, 10.)
else:
self.assertLess(loss_ratio, 0.1)
def testTrainedEnsemblePredCov(self, train_shape, test_shape, network,
out_logits):
training_steps = 1000
learning_rate = 0.1
ensemble_size = 1024
init_fn, apply_fn, kernel_fn = stax.serial(
stax.Dense(128, W_std=1.2, b_std=0.05), stax.Erf(),
stax.Dense(out_logits, W_std=1.2, b_std=0.05))
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
opt_update = jit(opt_update)
key, x_test, x_train, y_train = self._get_inputs(out_logits, test_shape,
train_shape)
predict_fn_mse_ens = predict.gradient_descent_mse_ensemble(
kernel_fn,
x_train,
y_train,
learning_rate=learning_rate,
diag_reg=0.)
train = (x_train, y_train)
ensemble_key = random.split(key, ensemble_size)
loss = jit(lambda params, x, y: 0.5 * np.mean((apply_fn(params, x) - y)**2))
grad_loss = jit(lambda state, x, y: grad(loss)(get_params(state), x, y))
def train_network(key):
_, params = init_fn(key, (-1,) + train_shape[1:])
opt_state = opt_init(params)
for i in range(training_steps):
opt_state = opt_update(i, grad_loss(opt_state, *train), opt_state)
return get_params(opt_state)
params = vmap(train_network)(ensemble_key)
rtol = 0.08
for x in [None, 'x_test']:
with self.subTest(x=x):
x = x if x is None else x_test
x_fin = x_train if x is None else x_test
ensemble_fx = vmap(apply_fn, (0, None))(params, x_fin)
mean_emp = np.mean(ensemble_fx, axis=0, keepdims=True)
mean_subtracted = ensemble_fx - mean_emp
cov_emp = np.einsum(
'ijk,ilk->jl', mean_subtracted, mean_subtracted, optimize=True) / (
mean_subtracted.shape[0] * mean_subtracted.shape[-1])
ntk = predict_fn_mse_ens(training_steps, x, 'ntk', compute_cov=True)
self._assertAllClose(mean_emp, ntk.mean, rtol)
self._assertAllClose(cov_emp, ntk.covariance, rtol)
def get_optimizer(optimizer, sched, b1=0.9, b2=0.999):
if optimizer.lower() == 'adagrad':
return optimizers.adagrad(sched)
elif optimizer.lower() == 'adam':
return optimizers.adam(sched, b1, b2)
elif optimizer.lower() == 'rmsprop':
return optimizers.rmsprop(sched)
elif optimizer.lower() == 'momentum':
return optimizers.momentum(sched, 0.9)
elif optimizer.lower() == 'sgd':
return optimizers.sgd(sched)
else:
raise Exception('Invalid optimizer: {}'.format(optimizer))
def main(unused_argv):
# Build data pipelines.
print('Loading data.')
x_train, y_train, x_test, y_test = \
datasets.get_dataset('mnist', FLAGS.train_size, FLAGS.test_size)
# Build the network
init_fn, apply_fn, _ = stax.serial(
stax.Dense(512, 1., 0.05),
stax.Erf(),
stax.Dense(10, 1., 0.05))
key = random.PRNGKey(0)
_, params = init_fn(key, (-1, 784))
# Create and initialize an optimizer.
opt_init, opt_apply, get_params = optimizers.sgd(FLAGS.learning_rate)
state = opt_init(params)
# Create an mse loss function and a gradient function.
loss = lambda fx, y_hat: 0.5 * np.mean((fx - y_hat) ** 2)
grad_loss = jit(grad(lambda params, x, y: loss(apply_fn(params, x), y)))
# Create an MSE predictor to solve the NTK equation in function space.
ntk = nt.batch(nt.empirical_ntk_fn(apply_fn, vmap_axes=0),
batch_size=4, device_count=0)
g_dd = ntk(x_train, None, params)
g_td = ntk(x_test, x_train, params)
predictor = nt.predict.gradient_descent_mse(g_dd, y_train)
# Get initial values of the network in function space.
fx_train = apply_fn(params, x_train)
fx_test = apply_fn(params, x_test)
# Train the network.
train_steps = int(FLAGS.train_time // FLAGS.learning_rate)
print('Training for {} steps'.format(train_steps))
for i in range(train_steps):
params = get_params(state)
state = opt_apply(i, grad_loss(params, x_train, y_train), state)
# Get predictions from analytic computation.
print('Computing analytic prediction.')
fx_train, fx_test = predictor(FLAGS.train_time, fx_train, fx_test, g_td)
# Print out summary data comparing the linear / nonlinear model.
util.print_summary('train', y_train, apply_fn(params, x_train), fx_train,
loss)
util.print_summary('test', y_test, apply_fn(params, x_test), fx_test,
loss)
def optimizer(name="adam",
momentum_mass=0.9, rmsprop_gamma=0.9, rmsprop_eps=1e-8,
adam_b1=0.9, adam_b2=0.997, adam_eps=1e-8):
"""Return the optimizer, by name."""
if name == "sgd":
return optimizers.sgd(learning_rate)
if name == "momentum":
return optimizers.momentum(learning_rate, mass=momentum_mass)
if name == "rmsprop":
return optimizers.rmsprop(
learning_rate, gamma=rmsprop_gamma, eps=rmsprop_eps)
if name == "adam":
return optimizers.adam(learning_rate, b1=adam_b1, b2=adam_b2, eps=adam_eps)
raise ValueError("Unknown optimizer %s" % str(name))
def testTracedStepSize(self):
def loss(x): return jnp.dot(x, x)
x0 = jnp.ones(2)
step_size = 0.1
init_fun, _, _ = optimizers.sgd(step_size)
opt_state = init_fun(x0)
@jit
def update(opt_state, step_size):
_, update_fun, get_params = optimizers.sgd(step_size)
x = get_params(opt_state)
g = grad(loss)(x)
return update_fun(0, g, opt_state)
update(opt_state, 0.9) # doesn't crash
def main(unused_argv):
# Build data pipelines.
print('Loading data.')
x_train, y_train, x_test, y_test = \
datasets.mnist(FLAGS.train_size, FLAGS.test_size)
# Build the network
init_fn, f = stax.serial(layers.Dense(4096), stax.Tanh, layers.Dense(10))
key = random.PRNGKey(0)
_, params = init_fn(key, (-1, 784))
# Create and initialize an optimizer.
opt_init, opt_apply, get_params = optimizers.sgd(FLAGS.learning_rate)
state = opt_init(params)
# Create an mse loss function and a gradient function.
loss = lambda fx, y_hat: 0.5 * np.mean((fx - y_hat)**2)
grad_loss = jit(grad(lambda params, x, y: loss(f(params, x), y)))
# Create an MSE predictor to solve the NTK equation in function space.
theta = tangents.ntk(f, batch_size=32)
g_dd = theta(params, x_train)
import ipdb
ipdb.set_trace()
g_td = theta(params, x_test, x_train)
predictor = tangents.analytic_mse_predictor(g_dd, y_train, g_td)
# Get initial values of the network in function space.
fx_train = f(params, x_train)
fx_test = f(params, x_test)
# Train the network.
train_steps = int(FLAGS.train_time // FLAGS.learning_rate)
print('Training for {} steps'.format(train_steps))
for i in range(train_steps):
params = get_params(state)
state = opt_apply(i, grad_loss(params, x_train, y_train), state)
# Get predictions from analytic computation.
print('Computing analytic prediction.')
fx_train, fx_test = predictor(fx_train, fx_test, FLAGS.train_time)
# Print out summary data comparing the linear / nonlinear model.
util.print_summary('train', y_train, f(params, x_train), fx_train, loss)
util.print_summary('test', y_test, f(params, x_test), fx_test, loss)
def testTracedStepSize(self):
def loss(x, _): return np.dot(x, x)
x0 = np.ones(2)
num_iters = 100
step_size = 0.1
init_fun, _ = optimizers.sgd(step_size)
opt_state = init_fun(x0)
@jit
def update(opt_state, step_size):
_, update_fun = optimizers.sgd(step_size)
x = optimizers.get_params(opt_state)
g = grad(loss)(x, None)
return update_fun(0, g, opt_state)
update(opt_state, 0.9) # doesn't crash
def relaxed_gd(X, phi_y, W_0, lr, beta_, lambda_, tol, N_max, verbose=True):
"""Run gradient descent on relaxed_loss."""
print(f"Lambda {lambda_}")
dim_H_ = phi_y.shape[1]
n_features_ = X.shape[1]
predictor_shape = (
dim_H_,
n_features_,
)
assert W_0.shape == predictor_shape
opt_init, opt_update, get_params = optimizers.sgd(lr)
opt_state = opt_init(W_0)
def step(opt_state, X, phi_y, beta, lambda_, i):
value, grads = jax.value_and_grad(relaxed_predict_loss)(
get_params(opt_state), X, phi_y, beta, lambda_)
if (i < 10 or i % 100 == 0) and verbose:
print(f"Step {i} value: \t\t{value.item()}")
opt_state = opt_update(i, grads, opt_state)
return value, opt_state
old_value = jnp.inf
diff = jnp.inf
step_index = 0
while step_index < N_max:
value, opt_state = step(opt_state, X, phi_y, beta_, lambda_,
step_index)
diff = abs(old_value - value)
old_value = value
step_index += 1
status = {
"max_steps": step_index >= N_max,
"tol_max": tol >= diff.item(),
"diff": diff.item(),
"step_index": step_index,
"final_value": value.item(),
}
if verbose:
print(status)
return get_params(opt_state), status
def privately_train(rng, params, predict, X, y):
"""Generic train function called for each slice.
Responsible for, given an rng key, a set of parameters to be trained, some inputs X and some outputs y,
finetuning the params on X and y according to some internally defined training configuration.
"""
locals().update(private_training_parameters)
def clipped_grad(params, l2_norm_clip, single_example_batch):
grads = grad(loss)(params, predict, single_example_batch)
nonempty_grads, tree_def = tree_flatten(grads)
total_grad_norm = np.linalg.norm([np.linalg.norm(neg.ravel()) for neg in nonempty_grads])
divisor = np.max((total_grad_norm / l2_norm_clip, 1.))
normalized_nonempty_grads = [g / divisor for g in nonempty_grads]
return tree_unflatten(tree_def, normalized_nonempty_grads)
def private_grad(params, batch, rng, l2_norm_clip, noise_multiplier, batch_size):
# Add batch dimension for when each example is separated
batch = (np.expand_dims(batch[0], 1), np.expand_dims(batch[1], 1))
clipped_grads = vmap(clipped_grad, (None, None, 0))(params, l2_norm_clip, batch)
clipped_grads_flat, grads_treedef = tree_flatten(clipped_grads)
aggregated_clipped_grads = [np.sum(g, 0) for g in clipped_grads_flat]
rngs = random.split(rng, len(aggregated_clipped_grads))
noised_aggregated_clipped_grads = [g + l2_norm_clip * noise_multiplier * random.normal(r, g.shape) for r, g in zip(rngs, aggregated_clipped_grads)]
normalized_noised_aggregated_clipped_grads = [g / batch_size for g in noised_aggregated_clipped_grads]
return tree_unflatten(grads_treedef, normalized_noised_aggregated_clipped_grads)
@jit
def private_update(rng, i, opt_state, batch):
params = get_params(opt_state)
rng = random.fold_in(rng, i)
grads = private_grad(params, batch, rng, l2_norm_clip, noise_multiplier, batch_size)
return opt_update(i, grads, opt_state)
opt_init, opt_update, get_params = optimizers.sgd(step_size)
opt_state = opt_init(params)
batches = data_stream(rng, batch_size, X, y)
itercount = itertools.count()
for _ in range(iterations):
temp, rng = random.split(rng)
opt_state = private_update(temp, next(itercount), opt_state, next(batches))
return get_params(opt_state)
def run_optimiser(Niters, l_rate, x_data, y_data, params_IC):
if optimiser_type == "sgd":
opt_init, opt_update, get_params = optimizers.sgd(l_rate)
elif optimiser_type == "adam":
opt_init, opt_update, get_params = optimizers.adam(l_rate)
else:
raise ValueError("Optimiser not added.")
@progress_bar_scan(Niters)
def body(state, step):
loss_val, loss_grad = val_and_grad_loss(get_params(state), x_data,
y_data)
state = opt_update(step, loss_grad, state)
return state, loss_val
state, loss_array = lax.scan(body, opt_init(params_IC),
jnp.arange(Niters))
return get_params(state), loss_array
def test_sgd(self):
# experimental/optimizers.py
jax_params = self.init_params
opt_init, opt_update, get_params = optimizers.sgd(LR)
state = opt_init(jax_params)
for i in range(STEPS):
state = opt_update(i, self.per_step_updates, state)
jax_params = get_params(state)
# experimental/optix.py
optix_params = self.init_params
sgd = optix.sgd(LR, 0.0)
state = sgd.init(optix_params)
for _ in range(STEPS):
updates, state = sgd.update(self.per_step_updates, state)
optix_params = optix.apply_updates(optix_params, updates)
# Check equivalence.
for x, y in zip(tree_leaves(jax_params), tree_leaves(optix_params)):
np.testing.assert_allclose(x, y, rtol=1e-5)
def get_optimizer(self, optim=None, stage='learn', step_size=None):
if optim is None:
if stage == 'learn':
optim = self.optim_learn
else:
optim = self.optim_proj
if step_size is None:
step_size = self.step_size
if optim == 1:
if self.verb > 2:
print("With momentum optimizer")
opt_init, opt_update, get_params = momentum(step_size=step_size,
mass=0.95)
elif optim == 2:
if self.verb > 2:
print("With rmsprop optimizer")
opt_init, opt_update, get_params = rmsprop(step_size,
gamma=0.9,
eps=1e-8)
elif optim == 3:
if self.verb > 2:
print("With adagrad optimizer")
opt_init, opt_update, get_params = adagrad(step_size, momentum=0.9)
elif optim == 4:
if self.verb > 2:
print("With Nesterov optimizer")
opt_init, opt_update, get_params = nesterov(step_size, 0.9)
elif optim == 5:
if self.verb > 2:
print("With SGD optimizer")
opt_init, opt_update, get_params = sgd(step_size)
else:
if self.verb > 2:
print("With adam optimizer")
opt_init, opt_update, get_params = adam(step_size)
return opt_init, opt_update, get_params
def gamma2kappa(g1, g2, kappa_shape, obj, step_size=0.01, n_iter=1000):
# Set up optimizer
init_kE = np.zeros(kappa_shape)
init_kB = np.zeros(kappa_shape)
init_params = (init_kE, init_kB)
opt_init, opt_update, get_params = optimizers.sgd(step_size=0.001)
opt_state = opt_init(init_params)
@jit
def update(i, opt_state):
params = get_params(opt_state)
gradient = grad(obj)(params, g1, g2)
return opt_update(i, gradient, opt_state)
# Loop
for t in range(n_iter):
opt_state = update(t, opt_state)
params = get_params(opt_state)
kEhat, kBhat = params
return kEhat, kBhat
def get_optimizer(
learning_rate: float = 1e-4, optimizer="sdg", optimizer_kwargs: dict = None
) -> JaxOptimizer:
"""Return a `JaxOptimizer` dataclass for a JAX optimizer
Args:
learning_rate (float, optional): Step size. Defaults to 1e-4.
optimizer (str, optional): Optimizer type (Allowed types: "adam",
"adamax", "adagrad", "rmsprop", "sdg"). Defaults to "sdg".
optimizer_kwargs (dict, optional): Additional keyword arguments
that are passed to the optimizer. Defaults to None.
Returns:
JaxOptimizer
"""
from jax.config import config # pylint:disable=import-outside-toplevel
config.update("jax_enable_x64", True)
from jax import jit # pylint:disable=import-outside-toplevel
from jax.experimental import optimizers # pylint:disable=import-outside-toplevel
if optimizer_kwargs is None:
optimizer_kwargs = {}
optimizer = optimizer.lower()
if optimizer == "adam":
opt_init, opt_update, get_params = optimizers.adam(learning_rate, **optimizer_kwargs)
elif optimizer == "adagrad":
opt_init, opt_update, get_params = optimizers.adagrad(learning_rate, **optimizer_kwargs)
elif optimizer == "adamax":
opt_init, opt_update, get_params = optimizers.adamax(learning_rate, **optimizer_kwargs)
elif optimizer == "rmsprop":
opt_init, opt_update, get_params = optimizers.rmsprop(learning_rate, **optimizer_kwargs)
else:
opt_init, opt_update, get_params = optimizers.sgd(learning_rate, **optimizer_kwargs)
opt_update = jit(opt_update)
return JaxOptimizer(opt_init, opt_update, get_params)
def run_vmc(steps, step_size, diag_shift, n_samples):
opt = jaxopt.sgd(step_size)
# opt = nk.optimizer.Sgd(step_size)
sr = nk.optimizer.SR(lsq_solver="BDCSVD", diag_shift=diag_shift)
sr.store_rank_enabled = False # not supported by BDCSVD
sr.store_covariance_matrix_enabled = True
vmc = nk.Vmc(
hamiltonian=ha,
sampler=sa,
optimizer=opt,
n_samples=n_samples,
n_discard=min(n_samples // 10, 200),
sr=sr,
)
if mpi_rank == 0:
print(vmc.info())
print(HEADER_STRING)
for step in vmc.iter(steps, 1):
output(vmc, step)
def test_validate_optimizers():
"""Make sure we correctly check/standardize the optimizers."""
from jax import jit # pylint:disable=import-outside-toplevel
from jax.experimental import optimizers # pylint:disable=import-outside-toplevel
from pyepal.models.nt import JaxOptimizer # pylint:disable=import-outside-toplevel
opt_init, opt_update, get_params = optimizers.sgd(1e-3)
opt_update = jit(opt_update)
optimizer = JaxOptimizer(opt_init, opt_update, get_params)
optimizers = [
JaxOptimizer(opt_init, opt_update, get_params),
JaxOptimizer(opt_init, opt_update, get_params),
]
with pytest.raises(ValueError):
validate_optimizers(opt_init, 2)
with pytest.raises(ValueError):
validate_optimizers([optimizer], 2)
assert validate_optimizers(optimizers, 2) == optimizers
with pytest.raises(ValueError):
validate_optimizers(optimizer, 2)
def update(opt_state, step_size):
_, update_fun, get_params = optimizers.sgd(step_size)
x = get_params(opt_state)
g = grad(loss)(x)
return update_fun(0, g, opt_state)
st.pyplot()
plt.close()
st.header("Comparison against finite SGD-NNs")
"""
Finally, let's bring back the practioner loved Neural Networks for a comparison.
"""
learning_rate = st.slider("Learning rate",
1e-4,
1.0,
0.1,
step=1e-4,
format="%.4f")
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
opt_update = jit(opt_update)
loss = jit(lambda params, x, y: 0.5 * np.mean((apply_fn(params, x) - y)**2))
grad_loss = jit(lambda state, x, y: grad(loss)(get_params(state), x, y))
train_losses = []
test_losses = []
opt_state = opt_init(params)
for i in range(training_steps):
opt_state = opt_update(i, grad_loss(opt_state, *train), opt_state)
train_losses += [loss(get_params(opt_state), *train)]
test_losses += [loss(get_params(opt_state), *test)]
def main(_):
sns.set()
sns.set_palette(sns.color_palette('hls', 10))
npr.seed(FLAGS.seed)
logging.info('Starting experiment.')
# Create model folder for outputs
try:
gfile.MakeDirs(FLAGS.work_dir)
except gfile.GOSError:
pass
stdout_log = gfile.Open('{}/stdout.log'.format(FLAGS.work_dir), 'w+')
# use mean/std of svhn train
train_images, _, _ = datasets.get_dataset_split(
name=FLAGS.train_split.split('-')[0],
split=FLAGS.train_split.split('-')[1],
shuffle=False)
train_mu, train_std = onp.mean(train_images), onp.std(train_images)
del train_images
# BEGIN: fetch test data and candidate pool
test_images, test_labels, _ = datasets.get_dataset_split(
name=FLAGS.test_split.split('-')[0],
split=FLAGS.test_split.split('-')[1],
shuffle=False)
pool_images, pool_labels, _ = datasets.get_dataset_split(
name=FLAGS.pool_split.split('-')[0],
split=FLAGS.pool_split.split('-')[1],
shuffle=False)
n_pool = len(pool_images)
test_images = (test_images -
train_mu) / train_std # normalize w train mu/std
pool_images = (pool_images -
train_mu) / train_std # normalize w train mu/std
# augmentation for train/pool data
if FLAGS.augment_data:
augmentation = data.chain_transforms(data.RandomHorizontalFlip(0.5),
data.RandomCrop(4), data.ToDevice)
else:
augmentation = None
# END: fetch test data and candidate pool
# BEGIN: load ckpt
opt_init, opt_update, get_params = optimizers.sgd(FLAGS.learning_rate)
if FLAGS.pretrained_dir is not None:
with gfile.Open(FLAGS.pretrained_dir, 'rb') as fpre:
pretrained_opt_state = optimizers.pack_optimizer_state(
pickle.load(fpre))
fixed_params = get_params(pretrained_opt_state)[:7]
ckpt_dir = '{}/{}'.format(FLAGS.root_dir, FLAGS.ckpt_idx)
with gfile.Open(ckpt_dir, 'wr') as fckpt:
opt_state = optimizers.pack_optimizer_state(pickle.load(fckpt))
params = get_params(opt_state)
# combine fixed pretrained params and dpsgd trained last layers
params = fixed_params + params
opt_state = opt_init(params)
else:
ckpt_dir = '{}/{}'.format(FLAGS.root_dir, FLAGS.ckpt_idx)
with gfile.Open(ckpt_dir, 'wr') as fckpt:
opt_state = optimizers.pack_optimizer_state(pickle.load(fckpt))
params = get_params(opt_state)
stdout_log.write('finetune from: {}\n'.format(ckpt_dir))
logging.info('finetune from: %s', ckpt_dir)
test_acc, test_pred = accuracy(params,
shape_as_image(test_images, test_labels),
return_predicted_class=True)
logging.info('test accuracy: %.2f', test_acc)
stdout_log.write('test accuracy: {}\n'.format(test_acc))
stdout_log.flush()
# END: load ckpt
# BEGIN: setup for dp model
@jit
def update(_, i, opt_state, batch):
params = get_params(opt_state)
return opt_update(i, grad_loss(params, batch), opt_state)
@jit
def private_update(rng, i, opt_state, batch):
params = get_params(opt_state)
rng = random.fold_in(rng, i) # get new key for new random numbers
return opt_update(
i,
private_grad(params, batch, rng, FLAGS.l2_norm_clip,
FLAGS.noise_multiplier, FLAGS.batch_size), opt_state)
# END: setup for dp model
### BEGIN: prepare extra points picked from pool data
# BEGIN: on pool data
pool_embeddings = [apply_fn_0(params[:-1],
pool_images[b_i:b_i + FLAGS.batch_size]) \
for b_i in range(0, n_pool, FLAGS.batch_size)]
pool_embeddings = np.concatenate(pool_embeddings, axis=0)
pool_logits = apply_fn_1(params[-1:], pool_embeddings)
pool_true_labels = np.argmax(pool_labels, axis=1)
pool_predicted_labels = np.argmax(pool_logits, axis=1)
pool_correct_indices = \
onp.where(pool_true_labels == pool_predicted_labels)[0]
pool_incorrect_indices = \
onp.where(pool_true_labels != pool_predicted_labels)[0]
assert len(pool_correct_indices) + \
len(pool_incorrect_indices) == len(pool_labels)
pool_probs = stax.softmax(pool_logits)
if FLAGS.uncertain == 0 or FLAGS.uncertain == 'entropy':
pool_entropy = -onp.sum(pool_probs * onp.log(pool_probs), axis=1)
stdout_log.write('all {} entropy: min {}, max {}\n'.format(
len(pool_entropy), onp.min(pool_entropy), onp.max(pool_entropy)))
pool_entropy_sorted_indices = onp.argsort(pool_entropy)
# take the n_extra most uncertain points
pool_uncertain_indices = \
pool_entropy_sorted_indices[::-1][:FLAGS.n_extra]
stdout_log.write('uncertain {} entropy: min {}, max {}\n'.format(
len(pool_entropy[pool_uncertain_indices]),
onp.min(pool_entropy[pool_uncertain_indices]),
onp.max(pool_entropy[pool_uncertain_indices])))
elif FLAGS.uncertain == 1 or FLAGS.uncertain == 'difference':
# 1st_prob - 2nd_prob
assert len(pool_probs.shape) == 2
sorted_pool_probs = onp.sort(pool_probs, axis=1)
pool_probs_diff = sorted_pool_probs[:, -1] - sorted_pool_probs[:, -2]
assert min(pool_probs_diff) > 0.
stdout_log.write('all {} difference: min {}, max {}\n'.format(
len(pool_probs_diff), onp.min(pool_probs_diff),
onp.max(pool_probs_diff)))
pool_uncertain_indices = onp.argsort(pool_probs_diff)[:FLAGS.n_extra]
stdout_log.write('uncertain {} difference: min {}, max {}\n'.format(
len(pool_probs_diff[pool_uncertain_indices]),
onp.min(pool_probs_diff[pool_uncertain_indices]),
onp.max(pool_probs_diff[pool_uncertain_indices])))
elif FLAGS.uncertain == 2 or FLAGS.uncertain == 'random':
pool_uncertain_indices = npr.permutation(n_pool)[:FLAGS.n_extra]
# END: on pool data
### END: prepare extra points picked from pool data
finetune_images = copy.deepcopy(pool_images[pool_uncertain_indices])
finetune_labels = copy.deepcopy(pool_labels[pool_uncertain_indices])
stdout_log.write('Starting fine-tuning...\n')
logging.info('Starting fine-tuning...')
stdout_log.flush()
stdout_log.write('{} points picked via {}\n'.format(
len(finetune_images), FLAGS.uncertain))
logging.info('%d points picked via %s', len(finetune_images),
FLAGS.uncertain)
assert FLAGS.n_extra == len(finetune_images)
for epoch in range(1, FLAGS.epochs + 1):
# BEGIN: finetune model with extra data, evaluate and save
num_extra = len(finetune_images)
num_complete_batches, leftover = divmod(num_extra, FLAGS.batch_size)
num_batches = num_complete_batches + bool(leftover)
finetune = data.DataChunk(X=finetune_images,
Y=finetune_labels,
image_size=32,
image_channels=3,
label_dim=1,
label_format='numeric')
batches = data.minibatcher(finetune,
FLAGS.batch_size,
transform=augmentation)
itercount = itertools.count()
key = random.PRNGKey(FLAGS.seed)
start_time = time.time()
for _ in range(num_batches):
# tmp_time = time.time()
b = next(batches)
if FLAGS.dpsgd:
opt_state = private_update(
key, next(itercount), opt_state,
shape_as_image(b.X, b.Y, dummy_dim=True))
else:
opt_state = update(key, next(itercount), opt_state,
shape_as_image(b.X, b.Y))
# stdout_log.write('single update in {:.2f} sec\n'.format(
# time.time() - tmp_time))
epoch_time = time.time() - start_time
stdout_log.write('Epoch {} in {:.2f} sec\n'.format(epoch, epoch_time))
logging.info('Epoch %d in %.2f sec', epoch, epoch_time)
# accuracy on test data
params = get_params(opt_state)
test_pred_0 = test_pred
test_acc, test_pred = accuracy(params,
shape_as_image(test_images,
test_labels),
return_predicted_class=True)
test_loss = loss(params, shape_as_image(test_images, test_labels))
stdout_log.write(
'Eval set loss, accuracy (%): ({:.2f}, {:.2f})\n'.format(
test_loss, 100 * test_acc))
logging.info('Eval set loss, accuracy: (%.2f, %.2f)', test_loss,
100 * test_acc)
stdout_log.flush()
# visualize prediction difference between 2 checkpoints.
if FLAGS.visualize:
utils.visualize_ckpt_difference(test_images,
np.argmax(test_labels, axis=1),
test_pred_0,
test_pred,
epoch - 1,
epoch,
FLAGS.work_dir,
mu=train_mu,
sigma=train_std)
# END: finetune model with extra data, evaluate and save
stdout_log.close()
def main(_):
if FLAGS.microbatches:
raise NotImplementedError(
'Microbatches < batch size not currently supported'
)
train_images, train_labels, test_images, test_labels = datasets.mnist()
num_train = train_images.shape[0]
num_complete_batches, leftover = divmod(num_train, FLAGS.batch_size)
num_batches = num_complete_batches + bool(leftover)
key = random.PRNGKey(FLAGS.seed)
def data_stream():
rng = npr.RandomState(FLAGS.seed)
while True:
perm = rng.permutation(num_train)
for i in range(num_batches):
batch_idx = perm[i * FLAGS.batch_size:(i + 1) * FLAGS.batch_size]
yield train_images[batch_idx], train_labels[batch_idx]
batches = data_stream()
opt_init, opt_update, get_params = optimizers.sgd(FLAGS.learning_rate)
@jit
def update(_, i, opt_state, batch):
params = get_params(opt_state)
return opt_update(i, grad(loss)(params, batch), opt_state)
@jit
def private_update(rng, i, opt_state, batch):
params = get_params(opt_state)
rng = random.fold_in(rng, i) # get new key for new random numbers
return opt_update(
i,
private_grad(params, batch, rng, FLAGS.l2_norm_clip,
FLAGS.noise_multiplier, FLAGS.batch_size), opt_state)
_, init_params = init_random_params(key, (-1, 28, 28, 1))
opt_state = opt_init(init_params)
itercount = itertools.count()
steps_per_epoch = 60000 // FLAGS.batch_size
print('\nStarting training...')
for epoch in range(1, FLAGS.epochs + 1):
start_time = time.time()
# pylint: disable=no-value-for-parameter
for _ in range(num_batches):
if FLAGS.dpsgd:
opt_state = \
private_update(
key, next(itercount), opt_state,
shape_as_image(*next(batches), dummy_dim=True))
else:
opt_state = update(
key, next(itercount), opt_state, shape_as_image(*next(batches)))
# pylint: enable=no-value-for-parameter
epoch_time = time.time() - start_time
print('Epoch {} in {:0.2f} sec'.format(epoch, epoch_time))
# evaluate test accuracy
params = get_params(opt_state)
test_acc = accuracy(params, shape_as_image(test_images, test_labels))
test_loss = loss(params, shape_as_image(test_images, test_labels))
print('Test set loss, accuracy (%): ({:.2f}, {:.2f})'.format(
test_loss, 100 * test_acc))
# determine privacy loss so far
if FLAGS.dpsgd:
delta = 1e-5
num_examples = 60000
eps = compute_epsilon(epoch * steps_per_epoch, num_examples, delta)
print(
'For delta={:.0e}, the current epsilon is: {:.2f}'.format(delta, eps))
else:
print('Trained with vanilla non-private SGD optimizer')
