def test_build_to_ids_fn_truncates(self):
vocab = ['A', 'B', 'C']
max_seq_len = 1
bos = stackoverflow_word_prediction.get_special_tokens(len(vocab)).bos
to_ids_fn = stackoverflow_word_prediction.build_to_ids_fn(
vocab, max_seq_len)
data = {'tokens': 'A B C'}
processed = to_ids_fn(data)
self.assertAllEqual(self.evaluate(processed), [bos, 1])
def test_oov_token_correct(self):
vocab = ['A', 'B', 'C']
max_seq_len = 5
num_oov_buckets = 2
to_ids_fn = stackoverflow_word_prediction.build_to_ids_fn(
vocab, max_seq_len, num_oov_buckets=num_oov_buckets)
oov_tokens = stackoverflow_word_prediction.get_special_tokens(
len(vocab), num_oov_buckets=num_oov_buckets).oov
data = {'tokens': 'A B D'}
processed = to_ids_fn(data)
self.assertLen(oov_tokens, num_oov_buckets)
self.assertIn(self.evaluate(processed)[3], oov_tokens)
def test_build_to_ids_fn_embeds_all_vocab(self):
vocab = ['A', 'B', 'C']
max_seq_len = 5
special_tokens = stackoverflow_word_prediction.get_special_tokens(
len(vocab))
bos = special_tokens.bos
eos = special_tokens.eos
to_ids_fn = stackoverflow_word_prediction.build_to_ids_fn(
vocab, max_seq_len)
data = {'tokens': 'A B C'}
processed = to_ids_fn(data)
self.assertAllEqual(self.evaluate(processed), [bos, 1, 2, 3, eos])
def test_pad_token_correct(self):
vocab = ['A', 'B', 'C']
max_seq_len = 5
to_ids_fn = stackoverflow_word_prediction.build_to_ids_fn(
vocab, max_seq_len)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
len(vocab))
pad, bos, eos = special_tokens.pad, special_tokens.bos, special_tokens.eos
data = {'tokens': 'A B C'}
processed = to_ids_fn(data)
batched_ds = tf.data.Dataset.from_tensor_slices([processed]).padded_batch(
1, padded_shapes=[6])
sample_elem = next(iter(batched_ds))
self.assertAllEqual(self.evaluate(sample_elem), [[bos, 1, 2, 3, eos, pad]])
def _get_stackoverflow_metrics(vocab_size, num_oov_buckets):
"""Metrics for stackoverflow dataset."""
special_tokens = stackoverflow_dataset.get_special_tokens(
vocab_size, num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
return [
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_with_oov', masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov', masked_tokens=[pad_token] + oov_tokens),
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
]
def run_centralized(optimizer: tf.keras.optimizers.Optimizer,
num_epochs: int,
batch_size: int,
decay_epochs: Optional[int] = None,
lr_decay: Optional[float] = None,
vocab_size: int = 10000,
num_oov_buckets: int = 1,
d_embed: int = 96,
d_model: int = 512,
d_hidden: int = 2048,
num_heads: int = 8,
num_layers: int = 1,
max_position_encoding: int = 1000,
dropout: float = 0.1,
num_validation_examples: int = 10000,
sequence_length: int = 20,
experiment_name: str = 'centralized_stackoverflow',
root_output_dir: str = '/tmp/fedopt_guide',
hparams_dict: Optional[Mapping[str, Any]] = None,
max_batches: Optional[int] = None):
"""Trains an Transformer on the Stack Overflow next word prediction task.
Args:
optimizer: A `tf.keras.optimizers.Optimizer` used to perform training.
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.
vocab_size: Vocab size for normal tokens.
num_oov_buckets: Number of out of vocabulary buckets.
d_embed: Dimension of the token embeddings.
d_model: Dimension of features of MultiHeadAttention layers.
d_hidden: Dimension of hidden layers of the FFN.
num_heads: Number of attention heads.
num_layers: Number of Transformer blocks.
max_position_encoding: Maximum number of positions for position embeddings.
dropout: Dropout rate.
num_validation_examples: The number of test examples to use for validation.
sequence_length: The maximum number of words to take for each sequence.
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).
hparams_dict: A mapping with string keys representing the hyperparameters
and their values. If not None, this is written to CSV.
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.
"""
train_dataset, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size,
sequence_length,
train_batch_size=batch_size,
num_validation_examples=num_validation_examples,
num_oov_buckets=num_oov_buckets,
)
if max_batches and max_batches >= 1:
train_dataset = train_dataset.take(max_batches)
validation_dataset = validation_dataset.take(max_batches)
test_dataset = test_dataset.take(max_batches)
model = transformer_models.create_transformer_lm(
vocab_size=vocab_size,
num_oov_buckets=num_oov_buckets,
d_embed=d_embed,
d_model=d_model,
d_hidden=d_hidden,
num_heads=num_heads,
num_layers=num_layers,
max_position_encoding=max_position_encoding,
dropout=dropout,
name='stackoverflow-transformer')
special_tokens = stackoverflow_word_prediction.get_special_tokens(
vocab_size=vocab_size, num_oov_buckets=num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=optimizer,
metrics=[
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
])
centralized_training_loop.run(keras_model=model,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
test_dataset=test_dataset,
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 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,
vocab_size: Optional[int] = 10000,
num_oov_buckets: Optional[int] = 1,
sequence_length: Optional[int] = 20,
num_validation_examples: Optional[int] = 10000,
embedding_size: Optional[int] = 96,
latent_size: Optional[int] = 670,
num_layers: Optional[int] = 1,
shared_embedding: Optional[bool] = False,
max_batches: Optional[int] = None,
cache_dir: Optional[str] = None):
"""Trains an RNN on the Stack Overflow next word prediction task.
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.
vocab_size: Integer dictating the number of most frequent words to use in
the vocabulary.
num_oov_buckets: The number of out-of-vocabulary buckets to use.
sequence_length: The maximum number of words to take for each sequence.
num_validation_examples: The number of test examples to use for validation.
embedding_size: The dimension of the word embedding layer.
latent_size: The dimension of the latent units in the recurrent layers.
num_layers: The number of stacked recurrent layers to use.
shared_embedding: Boolean indicating whether to tie input and output
embeddings.
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.
"""
train_dataset, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size=vocab_size,
max_sequence_length=sequence_length,
train_batch_size=batch_size,
num_validation_examples=num_validation_examples,
num_oov_buckets=num_oov_buckets,
cache_dir=cache_dir)
if max_batches and max_batches >= 1:
train_dataset = train_dataset.take(max_batches)
validation_dataset = validation_dataset.take(max_batches)
test_dataset = test_dataset.take(max_batches)
model = stackoverflow_models.create_recurrent_model(
vocab_size=vocab_size,
num_oov_buckets=num_oov_buckets,
name='stackoverflow-lstm',
embedding_size=embedding_size,
latent_size=latent_size,
num_layers=num_layers,
shared_embedding=shared_embedding)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
vocab_size=vocab_size, num_oov_buckets=num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=optimizer,
metrics=[
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
])
centralized_training_loop.run(keras_model=model,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
test_dataset=test_dataset,
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 run_federated(
iterative_process_builder: Callable[...,
tff.templates.IterativeProcess],
client_epochs_per_round: int,
client_batch_size: int,
clients_per_round: int,
client_datasets_random_seed: Optional[int] = None,
vocab_size: Optional[int] = 10000,
num_oov_buckets: Optional[int] = 1,
sequence_length: Optional[int] = 20,
max_elements_per_user: Optional[int] = 1000,
num_validation_examples: Optional[int] = 10000,
embedding_size: Optional[int] = 96,
latent_size: Optional[int] = 670,
num_layers: Optional[int] = 1,
shared_embedding: Optional[bool] = False,
total_rounds: Optional[int] = 1500,
experiment_name: Optional[str] = 'federated_so_nwp',
root_output_dir: Optional[str] = '/tmp/fed_opt',
**kwargs):
"""Runs an iterative process on the Stack Overflow next word 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.
The iterative process must also have a callable attribute `get_model_weights`
that takes as input the state of the iterative process, and returns a
`tff.learning.ModelWeights` object.
Args:
iterative_process_builder: A function that accepts a no-arg `model_fn`, 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.
client_datasets_random_seed: An optional int used to seed which clients are
sampled at each round. If `None`, no seed is used.
vocab_size: Integer dictating the number of most frequent words to use in
the vocabulary.
num_oov_buckets: The number of out-of-vocabulary buckets to use.
sequence_length: The maximum number of words to take for each sequence.
max_elements_per_user: The maximum number of elements processed for each
client's dataset.
num_validation_examples: The number of test examples to use for validation.
embedding_size: The dimension of the word embedding layer.
latent_size: The dimension of the latent units in the recurrent layers.
num_layers: The number of stacked recurrent layers to use.
shared_embedding: Boolean indicating whether to tie input and output
embeddings.
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.
**kwargs: Additional arguments configuring the training loop. For details
on supported arguments, see
`federated_research/utils/training_utils.py`.
"""
model_builder = functools.partial(
stackoverflow_models.create_recurrent_model,
vocab_size=vocab_size,
num_oov_buckets=num_oov_buckets,
embedding_size=embedding_size,
latent_size=latent_size,
num_layers=num_layers,
shared_embedding=shared_embedding)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
vocab_size, num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
def metrics_builder():
return [
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
# Notice BOS never appears in ground truth.
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
keras_metrics.NumBatchesCounter(),
keras_metrics.NumTokensCounter(masked_tokens=[pad_token])
]
train_clientdata, _, _ = tff.simulation.datasets.stackoverflow.load_data()
# TODO(b/161914546): consider moving evaluation to use
# `tff.learning.build_federated_evaluation` to get metrics over client
# distributions, as well as the example weight means from this centralized
# evaluation.
_, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size=vocab_size,
max_sequence_length=sequence_length,
num_validation_examples=num_validation_examples,
num_oov_buckets=num_oov_buckets)
train_dataset_preprocess_comp = stackoverflow_word_prediction.create_preprocess_fn(
vocab=stackoverflow_word_prediction.create_vocab(vocab_size),
num_oov_buckets=num_oov_buckets,
client_batch_size=client_batch_size,
client_epochs_per_round=client_epochs_per_round,
max_sequence_length=sequence_length,
max_elements_per_client=max_elements_per_user)
input_spec = train_dataset_preprocess_comp.type_signature.result.element
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())
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)
iterative_process = iterative_process_builder(
tff_model_fn, client_weight_fn=client_weight_fn)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_dataset_preprocess_comp, iterative_process)
training_process.get_model_weights = iterative_process.get_model_weights
client_datasets_fn = training_utils.build_client_datasets_fn(
dataset=train_clientdata,
clients_per_round=clients_per_round,
random_seed=client_datasets_random_seed)
evaluate_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
eval_dataset=validation_dataset,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
validation_fn = lambda model_weights, round_num: evaluate_fn(model_weights)
test_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
# Use both val and test for symmetry with other experiments, which
# evaluate on the entire test set.
eval_dataset=validation_dataset.concatenate(test_dataset),
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=validation_fn,
test_fn=test_fn,
total_rounds=total_rounds,
experiment_name=experiment_name,
root_output_dir=root_output_dir,
**kwargs)
def configure_training(
task_spec: training_specs.TaskSpec,
vocab_size: int = 10000,
num_oov_buckets: int = 1,
sequence_length: int = 20,
max_elements_per_user: int = 1000,
num_validation_examples: int = 10000,
embedding_size: int = 96,
latent_size: int = 670,
num_layers: int = 1,
shared_embedding: bool = False) -> training_specs.RunnerSpec:
"""Configures training for Stack Overflow next-word prediction.
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.
vocab_size: Integer dictating the number of most frequent words to use in
the vocabulary.
num_oov_buckets: The number of out-of-vocabulary buckets to use.
sequence_length: The maximum number of words to take for each sequence.
max_elements_per_user: The maximum number of elements processed for each
client's dataset.
num_validation_examples: The number of test examples to use for validation.
embedding_size: The dimension of the word embedding layer.
latent_size: The dimension of the latent units in the recurrent layers.
num_layers: The number of stacked recurrent layers to use.
shared_embedding: Boolean indicating whether to tie input and output
embeddings.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
model_builder = functools.partial(
stackoverflow_models.create_recurrent_model,
vocab_size=vocab_size,
num_oov_buckets=num_oov_buckets,
embedding_size=embedding_size,
latent_size=latent_size,
num_layers=num_layers,
shared_embedding=shared_embedding)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
vocab_size, num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
def metrics_builder():
return [
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
# Notice BOS never appears in ground truth.
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
keras_metrics.NumBatchesCounter(),
keras_metrics.NumTokensCounter(masked_tokens=[pad_token])
]
train_clientdata, _, _ = tff.simulation.datasets.stackoverflow.load_data()
# TODO(b/161914546): consider moving evaluation to use
# `tff.learning.build_federated_evaluation` to get metrics over client
# distributions, as well as the example weight means from this centralized
# evaluation.
_, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size=vocab_size,
max_sequence_length=sequence_length,
num_validation_examples=num_validation_examples,
num_oov_buckets=num_oov_buckets)
train_dataset_preprocess_comp = stackoverflow_word_prediction.create_preprocess_fn(
vocab=stackoverflow_word_prediction.create_vocab(vocab_size),
num_oov_buckets=num_oov_buckets,
client_batch_size=task_spec.client_batch_size,
client_epochs_per_round=task_spec.client_epochs_per_round,
max_sequence_length=sequence_length,
max_elements_per_client=max_elements_per_user)
input_spec = train_dataset_preprocess_comp.type_signature.result.element
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)
@tff.tf_computation(tf.string)
def train_dataset_computation(client_id):
client_train_data = train_clientdata.dataset_computation(client_id)
return train_dataset_preprocess_comp(client_train_data)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_dataset_computation, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
train_clientdata.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))
training_process.get_model_weights = iterative_process.get_model_weights
centralized_validation_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
eval_dataset=validation_dataset,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
def validation_fn(server_state, round_num):
del round_num
return centralized_validation_fn(
iterative_process.get_model_weights(server_state))
centralized_test_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
# Use both val and test for symmetry with other experiments, which
# evaluate on the entire test set.
eval_dataset=validation_dataset.concatenate(test_dataset),
loss_builder=loss_builder,
metrics_builder=metrics_builder)
def test_fn(server_state):
return centralized_test_fn(
iterative_process.get_model_weights(server_state))
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_elements_per_user: int,
total_rounds: int = 3000,
vocab_size: int = 10000,
num_oov_buckets: int = 1,
sequence_length: int = 20,
num_validation_examples: int = 10000,
dim_embed: int = 96,
dim_model: int = 512,
dim_hidden: int = 2048,
num_heads: int = 8,
num_layers: int = 1,
max_position_encoding: int = 1000,
dropout: float = 0.1,
client_datasets_random_seed: Optional[int] = None,
experiment_name: str = 'federated_stackoverflow',
root_output_dir: str = '/tmp/fedopt_guide',
max_val_test_batches: Optional[int] = None,
**kwargs) -> None:
"""Configures training for Stack Overflow next-word prediction.
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/fedopt_guide/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.
The iterative process must also have a callable attribute `get_model_weights`
that takes as input the state of the iterative process, and returns a
`tff.learning.ModelWeights` object.
Args:
iterative_process_builder: A function that accepts a no-arg `model_fn`, 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_elements_per_user: The maximum number of elements processed for each
client's dataset. This has be to a positive value or -1 (which means that
all elements are taken for training).
total_rounds: The number of federated training rounds.
vocab_size: Integer dictating the number of most frequent words to use in
the vocabulary.
num_oov_buckets: The number of out-of-vocabulary buckets to use.
sequence_length: The maximum number of words to take for each sequence.
num_validation_examples: The number of test examples to use for validation.
dim_embed: An integer for the dimension of the token embeddings.
dim_model: An integer for the dimension of features of MultiHeadAttention
layers.
dim_hidden: An integer for the dimension of hidden layers of the FFN.
num_heads: An integer for the number of attention heads.
num_layers: An integer for the number of Transformer blocks.
max_position_encoding: Maximum number of positions for position embeddings.
dropout: Dropout rate.
client_datasets_random_seed: An optional int used to seed which clients are
sampled at each round. If `None`, no seed is used.
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_val_test_batches: If set to a positive integer, val and test datasets
are capped to at most that many batches. If set to None or a nonpositive
integer, the full datasets are used.
**kwargs: Additional arguments configuring the training loop. For details on
supported arguments, see
`federated_research/fedopt_guide/training_utils.py`.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
train_clientdata, _, _ = tff.simulation.datasets.stackoverflow.load_data()
_, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size=vocab_size,
max_sequence_length=sequence_length,
num_validation_examples=num_validation_examples,
num_oov_buckets=num_oov_buckets)
if max_val_test_batches and max_val_test_batches >= 1:
validation_dataset = validation_dataset.take(max_val_test_batches)
test_dataset = test_dataset.take(max_val_test_batches)
model_builder = functools.partial(
transformer_models.create_transformer_lm,
vocab_size=vocab_size,
num_oov_buckets=num_oov_buckets,
dim_embed=dim_embed,
dim_model=dim_model,
dim_hidden=dim_hidden,
num_heads=num_heads,
num_layers=num_layers,
max_position_encoding=max_position_encoding,
dropout=dropout,
name='stackoverflow-transformer')
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
vocab_size, num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
def metrics_builder():
return [
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
# Notice BOS never appears in ground truth.
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
keras_metrics.NumBatchesCounter(),
keras_metrics.NumTokensCounter(masked_tokens=[pad_token])
]
train_dataset_preprocess_comp = stackoverflow_word_prediction.create_preprocess_fn(
vocab=stackoverflow_word_prediction.create_vocab(vocab_size),
num_oov_buckets=num_oov_buckets,
client_batch_size=client_batch_size,
client_epochs_per_round=client_epochs_per_round,
max_sequence_length=sequence_length,
max_elements_per_client=max_elements_per_user)
input_spec = train_dataset_preprocess_comp.type_signature.result.element
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())
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)
iterative_process = iterative_process_builder(
tff_model_fn, client_weight_fn=client_weight_fn)
if hasattr(train_clientdata, 'dataset_computation'):
@tff.tf_computation(tf.string)
def train_dataset_computation(client_id):
client_train_data = train_clientdata.dataset_computation(client_id)
return train_dataset_preprocess_comp(client_train_data)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_dataset_computation, iterative_process)
client_ids_fn = tff.simulation.build_uniform_sampling_fn(
train_clientdata.client_ids,
size=clients_per_round,
replace=False,
random_seed=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_dataset_preprocess_comp, iterative_process)
client_sampling_fn = tff.simulation.build_uniform_client_sampling_fn(
dataset=train_clientdata,
clients_per_round=clients_per_round,
random_seed=client_datasets_random_seed)
training_process.get_model_weights = iterative_process.get_model_weights
evaluate_fn = tff.learning.build_federated_evaluation(tff_model_fn)
def validation_fn(model_weights, round_num):
del round_num
return evaluate_fn(model_weights, [validation_dataset])
def test_fn(model_weights):
return evaluate_fn(model_weights,
[validation_dataset.concatenate(test_dataset)])
logging.info('Training model:')
logging.info(model_builder().summary())
training_loop.run(iterative_process=training_process,
train_client_datasets_fn=client_sampling_fn,
evaluation_fn=validation_fn,
test_fn=test_fn,
total_rounds=total_rounds,
experiment_name=experiment_name,
root_output_dir=root_output_dir,
**kwargs)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Expected no command-line arguments, '
'got: {}'.format(argv))
tff.backends.native.set_local_execution_context(max_fanout=10)
model_builder = functools.partial(
stackoverflow_models.create_recurrent_model,
vocab_size=FLAGS.vocab_size,
embedding_size=FLAGS.embedding_size,
latent_size=FLAGS.latent_size,
num_layers=FLAGS.num_layers,
shared_embedding=FLAGS.shared_embedding)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
FLAGS.vocab_size)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
def metrics_builder():
return [
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
# Notice BOS never appears in ground truth.
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
keras_metrics.NumBatchesCounter(),
keras_metrics.NumTokensCounter(masked_tokens=[pad_token]),
]
train_dataset, _ = stackoverflow_word_prediction.get_federated_datasets(
vocab_size=FLAGS.vocab_size,
train_client_batch_size=FLAGS.client_batch_size,
train_client_epochs_per_round=FLAGS.client_epochs_per_round,
max_sequence_length=FLAGS.sequence_length,
max_elements_per_train_client=FLAGS.max_elements_per_user)
_, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size=FLAGS.vocab_size,
max_sequence_length=FLAGS.sequence_length,
num_validation_examples=FLAGS.num_validation_examples)
if FLAGS.uniform_weighting:
def client_weight_fn(local_outputs):
del local_outputs
return 1.0
else:
def client_weight_fn(local_outputs):
return tf.cast(tf.squeeze(local_outputs['num_tokens']), tf.float32)
def model_fn():
return tff.learning.from_keras_model(
model_builder(),
loss_builder(),
input_spec=validation_dataset.element_spec,
metrics=metrics_builder())
if FLAGS.noise_multiplier is not None:
if not FLAGS.uniform_weighting:
raise ValueError(
'Differential privacy is only implemented for uniform weighting.'
)
dp_query = tff.utils.build_dp_query(
clip=FLAGS.clip,
noise_multiplier=FLAGS.noise_multiplier,
expected_total_weight=FLAGS.clients_per_round,
adaptive_clip_learning_rate=FLAGS.adaptive_clip_learning_rate,
target_unclipped_quantile=FLAGS.target_unclipped_quantile,
clipped_count_budget_allocation=FLAGS.
clipped_count_budget_allocation,
expected_clients_per_round=FLAGS.clients_per_round)
weights_type = tff.learning.framework.weights_type_from_model(model_fn)
aggregation_process = tff.utils.build_dp_aggregate_process(
weights_type.trainable, dp_query)
else:
aggregation_process = None
server_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags(
'server')
client_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags(
'client')
iterative_process = tff.learning.build_federated_averaging_process(
model_fn=model_fn,
server_optimizer_fn=server_optimizer_fn,
client_weight_fn=client_weight_fn,
client_optimizer_fn=client_optimizer_fn,
aggregation_process=aggregation_process)
client_datasets_fn = training_utils.build_client_datasets_fn(
train_dataset, FLAGS.clients_per_round)
evaluate_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
eval_dataset=validation_dataset,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
validation_fn = lambda model_weights, round_num: evaluate_fn(model_weights)
test_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
# Use both val and test for symmetry with other experiments, which
# evaluate on the entire test set.
eval_dataset=validation_dataset.concatenate(test_dataset),
loss_builder=loss_builder,
metrics_builder=metrics_builder)
logging.info('Training model:')
logging.info(model_builder().summary())
hparam_dict = utils_impl.lookup_flag_values(utils_impl.get_hparam_flags())
training_loop_dict = utils_impl.lookup_flag_values(training_loop_flags)
training_loop.run(iterative_process=iterative_process,
client_datasets_fn=client_datasets_fn,
validation_fn=validation_fn,
test_fn=test_fn,
hparam_dict=hparam_dict,
**training_loop_dict)
def run_federated(
iterative_process_builder: Callable[..., tff.templates.IterativeProcess],
evaluation_computation_builder: Callable[..., tff.Computation],
client_batch_size: int,
clients_per_round: int,
global_variables_only: bool,
vocab_size: int = 10000,
num_oov_buckets: int = 1,
sequence_length: int = 20,
max_elements_per_user: int = 1000,
embedding_size: int = 96,
latent_size: int = 670,
num_layers: int = 1,
total_rounds: int = 1500,
experiment_name: str = 'federated_so_nwp',
root_output_dir: str = '/tmp/fed_recon',
split_dataset_strategy: str = federated_trainer_utils
.SPLIT_STRATEGY_AGGREGATED,
split_dataset_proportion: int = 2,
compose_dataset_computation: bool = False,
**kwargs):
"""Runs an iterative process on the Stack Overflow next word 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`.
This model only sends updates for its embeddings corresponding to the most
common words. Embeddings for out of vocabulary buckets are reconstructed on
device at the beginning of each round, and destroyed at the end of these
rounds.
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.
The iterative process must also have a callable attribute `get_model_weights`
that takes as input the state of the iterative process, and returns a
`tff.learning.ModelWeights` object.
Args:
iterative_process_builder: A function that accepts a no-arg `model_fn`, a
`loss_fn`, a `metrics_fn`, and a `client_weight_fn`, and returns a
`tff.templates.IterativeProcess`. The `model_fn` must return a
`reconstruction_model.ReconstructionModel`. See `federated_trainer.py` for
an example.
evaluation_computation_builder: A function that accepts a no-arg `model_fn`,
a loss_fn`, and a `metrics_fn`, and returns a `tff.Computation` for
federated reconstruction evaluation. The `model_fn` must return a
`reconstruction_model.ReconstructionModel`. See `federated_trainer.py` for
an example.
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.
global_variables_only: If True, the `ReconstructionModel` contains all model
variables as global variables. This can be useful for baselines involving
aggregating all variables.
vocab_size: Integer dictating the number of most frequent words to use in
the vocabulary.
num_oov_buckets: The number of out-of-vocabulary buckets to use.
sequence_length: The maximum number of words to take for each sequence.
max_elements_per_user: The maximum number of elements processed for each
client's dataset.
embedding_size: The dimension of the word embedding layer.
latent_size: The dimension of the latent units in the recurrent layers.
num_layers: The number of stacked recurrent layers to use.
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.
split_dataset_strategy: The method to use to split the data. Must be one of
`skip`, in which case every `split_dataset_proportion` example is used for
reconstruction, or `aggregated`, when the first
1/`split_dataset_proportion` proportion of the examples is used for
reconstruction.
split_dataset_proportion: Parameter controlling how much of the data is used
for reconstruction. If `split_dataset_proportion` is n, then 1 / n of the
data is used for reconstruction.
compose_dataset_computation: Whether to compose dataset computation with
training and evaluation computations. If True, may speed up experiments by
parallelizing dataset computations in multimachine setups. Not currently
supported in OSS.
**kwargs: Additional arguments configuring the training loop. For details on
supported arguments, see `training_loop.py`.
"""
loss_fn = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
vocab_size, num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
def metrics_fn():
return [
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_with_oov', masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov', masked_tokens=[pad_token] + oov_tokens),
# Notice BOS never appears in ground truth.
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
keras_metrics.NumBatchesCounter(),
keras_metrics.NumTokensCounter(masked_tokens=[pad_token])
]
train_clientdata, validation_clientdata, test_clientdata = (
tff.simulation.datasets.stackoverflow.load_data())
vocab = stackoverflow_word_prediction.create_vocab(vocab_size)
dataset_preprocess_comp = stackoverflow_dataset.create_preprocess_fn(
vocab=vocab,
num_oov_buckets=num_oov_buckets,
client_batch_size=client_batch_size,
max_sequence_length=sequence_length,
max_elements_per_client=max_elements_per_user,
feature_dtypes=train_clientdata.element_type_structure,
sort_by_date=True)
input_spec = dataset_preprocess_comp.type_signature.result.element
model_fn = functools.partial(
models.create_recurrent_reconstruction_model,
vocab_size=vocab_size,
num_oov_buckets=num_oov_buckets,
embedding_size=embedding_size,
latent_size=latent_size,
num_layers=num_layers,
input_spec=input_spec,
global_variables_only=global_variables_only)
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)
iterative_process = iterative_process_builder(
model_fn,
loss_fn=loss_fn,
metrics_fn=metrics_fn,
client_weight_fn=client_weight_fn,
dataset_split_fn_builder=functools.partial(
federated_trainer_utils.build_dataset_split_fn,
split_dataset_strategy=split_dataset_strategy,
split_dataset_proportion=split_dataset_proportion))
base_eval_computation = evaluation_computation_builder(
model_fn,
loss_fn=loss_fn,
metrics_fn=metrics_fn,
dataset_split_fn_builder=functools.partial(
federated_trainer_utils.build_dataset_split_fn,
split_dataset_strategy=split_dataset_strategy,
split_dataset_proportion=split_dataset_proportion))
if compose_dataset_computation:
# Compose dataset computations with client training and evaluation to avoid
# linear cost of computing centrally. This changes the expected input of
# the `IterativeProcess` and `tff.Computation` to be a list of client IDs
# instead of datasets.
training_process = (
tff.simulation.compose_dataset_computation_with_iterative_process(
dataset_preprocess_comp, iterative_process))
training_process = (
tff.simulation.compose_dataset_computation_with_iterative_process(
train_clientdata.dataset_computation, training_process))
training_process.get_model_weights = iterative_process.get_model_weights
base_eval_computation = (
tff.simulation.compose_dataset_computation_with_computation(
dataset_preprocess_comp, base_eval_computation))
val_computation = (
tff.simulation.compose_dataset_computation_with_computation(
validation_clientdata.dataset_computation, base_eval_computation))
test_computation = (
tff.simulation.compose_dataset_computation_with_computation(
test_clientdata.dataset_computation, base_eval_computation))
# Create client sampling functions for each of train/val/test.
# We need to sample client IDs, not datasets, and we do not need to apply
# `dataset_preprocess_comp` since this is applied as part of the training
# process and evaluation computation.
train_client_datasets_fn = federated_trainer_utils.build_list_sample_fn(
train_clientdata.client_ids, size=clients_per_round, replace=False)
val_client_datasets_fn = federated_trainer_utils.build_list_sample_fn(
validation_clientdata.client_ids, size=clients_per_round, replace=False)
test_client_datasets_fn = federated_trainer_utils.build_list_sample_fn(
test_clientdata.client_ids, size=clients_per_round, replace=False)
else:
training_process = iterative_process
val_computation = base_eval_computation
test_computation = base_eval_computation
# Apply dataset computations.
train_clientdata = train_clientdata.preprocess(dataset_preprocess_comp)
validation_clientdata = validation_clientdata.preprocess(
dataset_preprocess_comp)
test_clientdata = test_clientdata.preprocess(dataset_preprocess_comp)
# Create client sampling functions for each of train/val/test.
train_client_datasets_fn = functools.partial(
tff.simulation.build_uniform_sampling_fn(train_clientdata.client_ids),
size=clients_per_round)
val_client_datasets_fn = functools.partial(
tff.simulation.build_uniform_sampling_fn(
validation_clientdata.client_ids),
size=clients_per_round)
test_client_datasets_fn = functools.partial(
tff.simulation.build_uniform_sampling_fn(test_clientdata.client_ids),
size=clients_per_round)
# Create final evaluation functions to pass to `training_loop`.
val_fn = federated_trainer_utils.build_eval_fn(
evaluation_computation=val_computation,
client_datasets_fn=val_client_datasets_fn,
get_model=training_process.get_model_weights)
test_fn = federated_trainer_utils.build_eval_fn(
evaluation_computation=test_computation,
client_datasets_fn=test_client_datasets_fn,
get_model=training_process.get_model_weights)
test_fn = functools.partial(test_fn, round_num=0)
training_loop.run(
iterative_process=training_process,
client_datasets_fn=train_client_datasets_fn,
validation_fn=val_fn,
test_fn=test_fn,
total_rounds=total_rounds,
experiment_name=experiment_name,
root_output_dir=root_output_dir,
**kwargs)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Expected no command-line arguments, '
'got: {}'.format(argv))
tff.backends.native.set_local_execution_context(max_fanout=10)
model_builder = functools.partial(
stackoverflow_models.create_recurrent_model,
vocab_size=FLAGS.vocab_size,
embedding_size=FLAGS.embedding_size,
latent_size=FLAGS.latent_size,
num_layers=FLAGS.num_layers,
shared_embedding=FLAGS.shared_embedding)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
FLAGS.vocab_size)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
def metrics_builder():
return [
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
# Notice BOS never appears in ground truth.
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
keras_metrics.NumBatchesCounter(),
keras_metrics.NumTokensCounter(masked_tokens=[pad_token]),
]
train_dataset, _ = stackoverflow_word_prediction.get_federated_datasets(
vocab_size=FLAGS.vocab_size,
train_client_batch_size=FLAGS.client_batch_size,
train_client_epochs_per_round=FLAGS.client_epochs_per_round,
max_sequence_length=FLAGS.sequence_length,
max_elements_per_train_client=FLAGS.max_elements_per_user)
_, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size=FLAGS.vocab_size,
max_sequence_length=FLAGS.sequence_length,
num_validation_examples=FLAGS.num_validation_examples)
if FLAGS.uniform_weighting:
client_weighting = tff.learning.ClientWeighting.UNIFORM
else:
client_weighting = tff.learning.ClientWeighting.NUM_EXAMPLES
def model_fn():
return tff.learning.from_keras_model(
model_builder(),
loss_builder(),
input_spec=validation_dataset.element_spec,
metrics=metrics_builder())
if FLAGS.noise_multiplier is not None:
if not FLAGS.uniform_weighting:
raise ValueError(
'Differential privacy is only implemented for uniform weighting.'
)
if FLAGS.noise_multiplier <= 0:
raise ValueError(
'noise_multiplier must be positive if DP is enabled.')
if FLAGS.clip is None or FLAGS.clip <= 0:
raise ValueError('clip must be positive if DP is enabled.')
if not FLAGS.adaptive_clip_learning_rate:
aggregation_factory = tff.aggregators.DifferentiallyPrivateFactory.gaussian_fixed(
noise_multiplier=FLAGS.noise_multiplier,
clients_per_round=FLAGS.clients_per_round,
clip=FLAGS.clip)
else:
if FLAGS.adaptive_clip_learning_rate <= 0:
raise ValueError(
'adaptive_clip_learning_rate must be positive if '
'adaptive clipping is enabled.')
aggregation_factory = tff.aggregators.DifferentiallyPrivateFactory.gaussian_adaptive(
noise_multiplier=FLAGS.noise_multiplier,
clients_per_round=FLAGS.clients_per_round,
initial_l2_norm_clip=FLAGS.clip,
target_unclipped_quantile=FLAGS.target_unclipped_quantile,
learning_rate=FLAGS.adaptive_clip_learning_rate)
else:
if FLAGS.uniform_weighting:
aggregation_factory = tff.aggregators.UnweightedMeanFactory()
else:
aggregation_factory = tff.aggregators.MeanFactory()
server_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags(
'server')
client_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags(
'client')
iterative_process = tff.learning.build_federated_averaging_process(
model_fn=model_fn,
server_optimizer_fn=server_optimizer_fn,
client_weighting=client_weighting,
client_optimizer_fn=client_optimizer_fn,
model_update_aggregation_factory=aggregation_factory)
client_datasets_fn = training_utils.build_client_datasets_fn(
train_dataset, FLAGS.clients_per_round)
evaluate_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
eval_dataset=validation_dataset,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
validation_fn = lambda state, round_num: evaluate_fn(state.model)
evaluate_test_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
# Use both val and test for symmetry with other experiments, which
# evaluate on the entire test set.
eval_dataset=validation_dataset.concatenate(test_dataset),
loss_builder=loss_builder,
metrics_builder=metrics_builder)
test_fn = lambda state: evaluate_test_fn(state.model)
logging.info('Training model:')
logging.info(model_builder().summary())
# Log hyperparameters to CSV
hparam_dict = utils_impl.lookup_flag_values(utils_impl.get_hparam_flags())
results_dir = os.path.join(FLAGS.root_output_dir, 'results',
FLAGS.experiment_name)
utils_impl.create_directory_if_not_exists(results_dir)
hparam_file = os.path.join(results_dir, 'hparams.csv')
utils_impl.atomic_write_series_to_csv(hparam_dict, hparam_file)
training_loop.run(iterative_process=iterative_process,
client_datasets_fn=client_datasets_fn,
validation_fn=validation_fn,
test_fn=test_fn,
total_rounds=FLAGS.total_rounds,
experiment_name=FLAGS.experiment_name,
root_output_dir=FLAGS.root_output_dir,
rounds_per_eval=FLAGS.rounds_per_eval,
rounds_per_checkpoint=FLAGS.rounds_per_checkpoint)
