def test_preprocess_applied(self, mock_load_data):
if tf.config.list_logical_devices('GPU'):
self.skipTest('skip GPU test')
# Mock out the actual data loading from disk. Assert that the preprocessing
# function is applied to the client data, and that only the ClientData
# objects we desired are used.
#
# The correctness of the preprocessing function is tested in other tests.
sample_ds = tf.data.Dataset.from_tensor_slices(
collections.OrderedDict(
snippets=['a snippet', 'different snippet']))
mock_train = mock.create_autospec(tff.simulation.datasets.ClientData)
mock_train.create_tf_dataset_from_all_clients = mock.Mock(
return_value=sample_ds)
mock_test = mock.create_autospec(tff.simulation.datasets.ClientData)
mock_test.create_tf_dataset_from_all_clients = mock.Mock(
return_value=sample_ds)
mock_load_data.return_value = (mock_train, mock_test)
_, _ = shakespeare_dataset.get_centralized_datasets()
mock_load_data.assert_called_once()
# Assert the validation ClientData isn't used, and the train and test
# are amalgamated into datasets single datasets over all clients.
self.assertEqual(
mock_train.mock_calls,
mock.call.create_tf_dataset_from_all_clients().call_list())
self.assertEqual(
mock_test.mock_calls,
mock.call.create_tf_dataset_from_all_clients().call_list())
def run_centralized(optimizer: tf.keras.optimizers.Optimizer,
experiment_name: str,
root_output_dir: str,
num_epochs: int,
batch_size: int,
decay_epochs: Optional[int] = None,
lr_decay: Optional[float] = None,
hparams_dict: Optional[Mapping[str, Any]] = None,
sequence_length: Optional[int] = 80,
max_batches: Optional[int] = None):
"""Trains a two-layer RNN on Shakespeare next-character-prediction.
Args:
optimizer: A `tf.keras.optimizers.Optimizer` used to perform training.
experiment_name: The name of the experiment. Part of the output directory.
root_output_dir: The top-level output directory for experiment runs. The
`experiment_name` argument will be appended, and the directory will
contain tensorboard logs, metrics written as CSVs, and a CSV of
hyperparameter choices (if `hparams_dict` is used).
num_epochs: The number of training epochs.
batch_size: The batch size, used for train, validation, and test.
decay_epochs: The number of epochs of training before decaying the learning
rate. If None, no decay occurs.
lr_decay: The amount to decay the learning rate by after `decay_epochs`
training epochs have occurred.
hparams_dict: A mapping with string keys representing the hyperparameters
and their values. If not None, this is written to CSV.
sequence_length: The sequence length used for Shakespeare preprocessing.
max_batches: If set to a positive integer, datasets are capped to at most
that many batches. If set to None or a nonpositive integer, the full
datasets are used.
"""
shakespeare_train, shakespeare_test = shakespeare_dataset.get_centralized_datasets(
train_batch_size=batch_size, sequence_length=sequence_length)
if max_batches and max_batches >= 1:
shakespeare_train = shakespeare_train.take(max_batches)
shakespeare_test = shakespeare_test.take(max_batches)
pad_token, _, _, _ = shakespeare_dataset.get_special_tokens()
model = shakespeare_models.create_recurrent_model(
vocab_size=VOCAB_SIZE, sequence_length=sequence_length)
model.compile(
optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[
keras_metrics.MaskedCategoricalAccuracy(masked_tokens=[pad_token])
])
centralized_training_loop.run(keras_model=model,
train_dataset=shakespeare_train,
validation_dataset=shakespeare_test,
experiment_name=experiment_name,
root_output_dir=root_output_dir,
num_epochs=num_epochs,
hparams_dict=hparams_dict,
decay_epochs=decay_epochs,
lr_decay=lr_decay)
def configure_training(task_spec: training_specs.TaskSpec,
sequence_length: int = 80) -> training_specs.RunnerSpec:
"""Configures training for the Shakespeare next-character prediction task.
This method will load and pre-process datasets and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
sequence_length: An int specifying the length of the character sequences
used for prediction.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
shakespeare_train, _ = tff.simulation.datasets.shakespeare.load_data()
_, shakespeare_test = shakespeare_dataset.get_centralized_datasets(
sequence_length=sequence_length)
train_preprocess_fn = shakespeare_dataset.create_preprocess_fn(
num_epochs=task_spec.client_epochs_per_round,
batch_size=task_spec.client_batch_size,
sequence_length=sequence_length)
input_spec = train_preprocess_fn.type_signature.result.element
model_builder = functools.partial(create_shakespeare_model,
sequence_length=sequence_length)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
if hasattr(shakespeare_train, 'dataset_computation'):
@tff.tf_computation(tf.string)
def build_train_dataset_from_client_id(client_id):
client_dataset = shakespeare_train.dataset_computation(client_id)
return train_preprocess_fn(client_dataset)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
build_train_dataset_from_client_id, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
shakespeare_train.client_ids,
size=task_spec.clients_per_round,
replace=False,
random_seed=task_spec.client_datasets_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
else:
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_preprocess_fn, iterative_process)
client_sampling_fn = training_utils.build_client_datasets_fn(
dataset=shakespeare_train,
clients_per_round=task_spec.clients_per_round,
random_seed=task_spec.client_datasets_random_seed)
training_process.get_model_weights = iterative_process.get_model_weights
test_fn = training_utils.build_centralized_evaluate_fn(
eval_dataset=shakespeare_test,
model_builder=model_builder,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
validation_fn = lambda model_weights, round_num: test_fn(model_weights)
return training_specs.RunnerSpec(iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
def run_federated(
iterative_process_builder: Callable[...,
tff.templates.IterativeProcess],
client_epochs_per_round: int,
client_batch_size: int,
clients_per_round: int,
max_batches_per_client: Optional[int] = -1,
client_datasets_random_seed: Optional[int] = None,
sequence_length: Optional[int] = 80,
total_rounds: Optional[int] = 1500,
experiment_name: Optional[str] = 'federated_shakespeare',
root_output_dir: Optional[str] = '/tmp/fed_opt',
max_eval_batches: Optional[int] = None,
**kwargs):
"""Runs an iterative process on a Shakespeare next character prediction task.
This method will load and pre-process dataset and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process that it applies to the task, using
`federated_research.utils.training_loop`.
We assume that the iterative process has the following functional type
signatures:
* `initialize`: `( -> S@SERVER)` where `S` represents the server state.
* `next`: `<S@SERVER, {B*}@CLIENTS> -> <S@SERVER, T@SERVER>` where `S`
represents the server state, `{B*}` represents the client datasets,
and `T` represents a python `Mapping` object.
Moreover, the server state must have an attribute `model` of type
`tff.learning.ModelWeights`.
Args:
iterative_process_builder: A function that accepts a no-arg `model_fn`, and
a `client_weight_fn`, and returns a `tff.templates.IterativeProcess`. The
`model_fn` must return a `tff.learning.Model`.
client_epochs_per_round: An integer representing the number of epochs of
training performed per client in each training round.
client_batch_size: An integer representing the batch size used on clients.
clients_per_round: An integer representing the number of clients
participating in each round.
max_batches_per_client: An optional int specifying the number of batches
taken by each client at each round. If `-1`, the entire client dataset is
used.
client_datasets_random_seed: An optional int used to seed which clients are
sampled at each round. If `None`, no seed is used.
sequence_length: An int specifying the length of the character sequences
used for prediction.
total_rounds: The number of federated training rounds.
experiment_name: The name of the experiment being run. This will be appended
to the `root_output_dir` for purposes of writing outputs.
root_output_dir: The name of the root output directory for writing
experiment outputs.
max_eval_batches: If set to a positive integer, evaluation datasets are
capped to at most that many batches. If set to None or a nonpositive
integer, the full evaluation datasets are used.
**kwargs: Additional arguments configuring the training loop. For details
on supported arguments, see
`federated_research/utils/training_utils.py`.
"""
train_clientdata = shakespeare_dataset.construct_character_level_datasets(
client_batch_size=client_batch_size,
client_epochs_per_round=client_epochs_per_round,
sequence_length=sequence_length,
max_batches_per_client=max_batches_per_client)
_, test_dataset = shakespeare_dataset.get_centralized_datasets(
train_batch_size=client_batch_size,
max_test_batches=max_eval_batches,
sequence_length=sequence_length)
model_builder = functools.partial(create_shakespeare_model,
sequence_length=sequence_length)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
input_spec = train_clientdata.create_tf_dataset_for_client(
train_clientdata.client_ids[0]).element_spec
def client_weight_fn(local_outputs):
# Num_tokens is a tensor with type int64[1], to use as a weight need
# a float32 scalar.
return tf.cast(tf.squeeze(local_outputs['num_tokens']), tf.float32)
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
training_process = iterative_process_builder(
tff_model_fn, client_weight_fn=client_weight_fn)
client_datasets_fn = training_utils.build_client_datasets_fn(
train_dataset=train_clientdata,
train_clients_per_round=clients_per_round,
random_seed=client_datasets_random_seed)
evaluate_fn = training_utils.build_evaluate_fn(
eval_dataset=test_dataset,
model_builder=model_builder,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
logging.info('Training model:')
logging.info(model_builder().summary())
training_loop.run(iterative_process=training_process,
client_datasets_fn=client_datasets_fn,
validation_fn=evaluate_fn,
test_fn=evaluate_fn,
total_rounds=total_rounds,
total_clients=clients_per_round,
experiment_name=experiment_name,
root_output_dir=root_output_dir,
**kwargs)
