def client_computation(gen_inputs, real_data, from_server):
"""Returns the client_output."""
return gan_training_tf_fns.client_computation(
gen_inputs_ds=gen_inputs,
real_data_ds=real_data,
from_server=from_server,
generator=gan.generator_model_fn(),
discriminator=gan.discriminator_model_fn(),
train_discriminator_fn=gan.train_discriminator_fn)
def client_computation(gen_inputs, real_data, from_server,
control_input_gen, control_input_disc):
"""Returns the client_output."""
return gan_training_tf_fns.client_computation(
gen_inputs_ds=gen_inputs,
real_data_ds=real_data,
from_server=from_server,
generator=gan.generator_model_fn(),
discriminator=gan.discriminator_model_fn(),
gen_optimizer=gen_optimizer_fn(),
disc_optimizer=disc_optimizer_fn(),
control_input_gen=control_input_gen,
control_input_disc=control_input_disc,
tau=tau)
def test_client_and_server_computations(self):
train_generator_fn, train_discriminator_fn = (
_get_train_generator_and_discriminator_fns())
# N.B. The way we are using datasets and re-using the same
# generator and discriminator doesn't really "make sense" from an ML
# perspective, but it's sufficient for testing. For more proper usage of
# these functions, see training_loops.py.
generator = one_dim_gan.create_generator()
discriminator = one_dim_gan.create_discriminator()
gen_inputs = one_dim_gan.create_generator_inputs()
real_data = one_dim_gan.create_real_data()
server_state = gan_training_tf_fns.server_initial_state(
generator, discriminator)
# The aggregation state (e.g., used for handling DP averaging) is
# initialized to be empty. A user of the `server_initial_state` is expected
# to take the output `ServerState` object and populate this field, most
# likely via an instance of tff.templates.AggregationProcess.
self.assertEmpty(server_state.aggregation_state)
client_output = gan_training_tf_fns.client_computation(
gen_inputs.take(3), real_data.take(3),
gan_training_tf_fns.FromServer(
generator_weights=server_state.generator_weights,
discriminator_weights=server_state.discriminator_weights),
generator, discriminator, train_discriminator_fn)
server_disc_update_optimizer = tf.keras.optimizers.Adam()
for _ in range(2): # Train for 2 rounds
server_state = gan_training_tf_fns.server_computation(
server_state, gen_inputs.take(3), client_output, generator,
discriminator, server_disc_update_optimizer, train_generator_fn,
NEW_DP_AVERAGING_STATE)
counters = self.evaluate(server_state.counters)
self.assertDictEqual(
counters, {
'num_rounds': 2,
'num_discriminator_train_examples': 2 * 3 * one_dim_gan.BATCH_SIZE,
'num_generator_train_examples': 2 * 3 * one_dim_gan.BATCH_SIZE
})
# DP averaging aggregation state updates properly in server_computation().
self.assertEqual(server_state.aggregation_state, NEW_DP_AVERAGING_STATE)
def test_client_and_server_computations(self):
train_generator_fn, train_discriminator_fn = (
_get_train_generator_and_discriminator_fns())
# N.B. The way we are using datasets and re-using the same
# generator and discriminator doesn't really "make sense" from an ML
# perspective, but it's sufficient for testing. For more proper usage of
# these functions, see training_loops.py.
generator = one_dim_gan.create_generator()
discriminator = one_dim_gan.create_discriminator()
gen_inputs = one_dim_gan.create_generator_inputs()
real_data = one_dim_gan.create_real_data()
server_state = gan_training_tf_fns.server_initial_state(
generator, discriminator, INIT_DP_AVERAGING_STATE)
# DP averaging aggregation state is initialized properly in
# server_initial_state().
self.assertEqual(server_state.dp_averaging_state, INIT_DP_AVERAGING_STATE)
client_output = gan_training_tf_fns.client_computation(
gen_inputs.take(3), real_data.take(3),
gan_training_tf_fns.FromServer(
generator_weights=server_state.generator_weights,
discriminator_weights=server_state.discriminator_weights),
generator, discriminator, train_discriminator_fn)
server_disc_update_optimizer = tf.keras.optimizers.Adam()
for _ in range(2): # Train for 2 rounds
server_state = gan_training_tf_fns.server_computation(
server_state, gen_inputs.take(3), client_output, generator,
discriminator, server_disc_update_optimizer, train_generator_fn,
NEW_DP_AVERAGING_STATE)
counters = self.evaluate(server_state.counters)
self.assertDictEqual(
counters, {
'num_rounds': 2,
'num_discriminator_train_examples': 2 * 3 * one_dim_gan.BATCH_SIZE,
'num_generator_train_examples': 2 * 3 * one_dim_gan.BATCH_SIZE
})
# DP averaging aggregation state updates properly in server_computation().
self.assertEqual(server_state.dp_averaging_state, NEW_DP_AVERAGING_STATE)
def simple_training_loop(generator_model_fn,
discriminator_model_fn,
real_data_fn,
gen_inputs_fn,
train_generator_fn,
train_discriminator_fn,
total_rounds=30,
client_disc_train_steps=16,
server_gen_train_steps=8,
rounds_per_eval=10,
eval_hook=lambda *args: None):
"""Trains in TF using client_computation and server_computation.
This is not intended to be a general-purpose training loop (e.g., the
optimizers are hard-coded), it is primarily intended for testing.
Args:
generator_model_fn: A no-arg function return the generator model.
discriminator_model_fn: A no-arg function return the discriminator model.
real_data_fn: A no-arg function returning a dataset of real data batches.
gen_inputs_fn: A no-arg function returning a dataset of generator input
batches.
train_generator_fn: A function which takes the two networks and generator
input and trains the generator.
train_discriminator_fn: A function which takes the two networks, generator
input, and real data and trains the discriminator.
total_rounds: Number of rounds to train.
client_disc_train_steps: Number of discriminator training batches per round.
server_gen_train_steps: Number of generator training batches per round.
rounds_per_eval: How often to call the `eval_hook` function.
eval_hook: A function taking arguments (generator, discriminator,
server_state, round_num) and performs evaluation. Optional.
Returns:
A tuple (final `ServerState`, train_time_in_seconds).
"""
logging.info('Starting simple_training_loop')
# N.B. We can't use real_data.take(...) in the loops below,
# or we would get the same examples on every round. Using window
# essentially breaks one Dataset into a sequence of Datasets,
# which is exactly what we need here.
client_gen_inputs = iter(gen_inputs_fn().window(client_disc_train_steps))
client_real_data = iter(real_data_fn().window(client_disc_train_steps))
server_gen_inputs = iter(gen_inputs_fn().window(server_gen_train_steps))
server_generator = generator_model_fn()
server_discriminator = discriminator_model_fn()
# We could probably use a single copy of the generator and discriminator, but
# using separate copies is more faithful to how this code will be used in TFF.
client_generator = generator_model_fn()
client_discriminator = discriminator_model_fn()
server_disc_update_optimizer = tf.keras.optimizers.SGD(learning_rate=1.0)
server_state = gan_training_tf_fns.server_initial_state(
server_generator, server_discriminator)
start_time = time.time()
def do_eval(round_num):
eval_hook(server_generator, server_discriminator, server_state,
round_num)
elapsed_minutes = (time.time() - start_time) / 60
print('Total training time {:.2f} minutes for {} rounds '
'({:.2f} rounds per minute)'.format(elapsed_minutes, round_num,
round_num / elapsed_minutes),
flush=True)
logging.info('Starting training')
for round_num in range(total_rounds):
if round_num % rounds_per_eval == 0:
do_eval(round_num)
client_output = gan_training_tf_fns.client_computation(
gen_inputs_ds=next(client_gen_inputs),
real_data_ds=next(client_real_data),
from_server=gan_training_tf_fns.FromServer(
generator_weights=server_state.generator_weights,
discriminator_weights=server_state.discriminator_weights),
generator=client_generator,
discriminator=client_discriminator,
train_discriminator_fn=train_discriminator_fn)
server_state = gan_training_tf_fns.server_computation(
server_state=server_state,
gen_inputs_ds=next(server_gen_inputs),
client_output=client_output,
generator=server_generator,
discriminator=server_discriminator,
server_disc_update_optimizer=server_disc_update_optimizer,
train_generator_fn=train_generator_fn)
train_time = time.time() - start_time
do_eval(total_rounds)
return server_state, train_time
