def test_preprocess_fn_return_dataset_element_spec_oov_buckets(self): ds = tf.data.Dataset.from_tensor_slices(TEST_DATA) preprocess_fn = stackoverflow_word_prediction.create_preprocess_fn( client_batch_size=32, client_epochs_per_round=1, max_sequence_length=10, max_elements_per_client=100, vocab=['one', 'must'], num_oov_buckets=10) preprocessed_ds = preprocess_fn(ds) self.assertEqual(preprocessed_ds.element_spec, (tf.TensorSpec(shape=[None, 10], dtype=tf.int64), tf.TensorSpec(shape=[None, 10], dtype=tf.int64)))
def _preprocess_stackoverflow(vocab_size, num_oov_buckets, sequence_length, num_validation_examples, client_batch_size, client_epochs_per_round, max_elements_per_user): """Prepare stackoverflow dataset.""" train_clientdata, _, test_clientdata = ( tff.simulation.datasets.stackoverflow.load_data()) dataset_vocab = stackoverflow_dataset.create_vocab(vocab_size) base_test_dataset = test_clientdata.create_tf_dataset_from_all_clients() preprocess_val_and_test = stackoverflow_dataset.create_preprocess_fn( vocab=dataset_vocab, num_oov_buckets=num_oov_buckets, client_batch_size=128, client_epochs_per_round=client_epochs_per_round, max_sequence_length=sequence_length, max_elements_per_client=-1, max_shuffle_buffer_size=1) test_set = preprocess_val_and_test( base_test_dataset.skip(num_validation_examples)) validation_set = preprocess_val_and_test( base_test_dataset.take(num_validation_examples)) train_dataset_preprocess_comp = stackoverflow_dataset.create_preprocess_fn( vocab=dataset_vocab, 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, max_shuffle_buffer_size=max_elements_per_user) @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) return train_dataset_computation, train_clientdata, validation_set, test_set
def test_preprocess_fn_returns_correct_sequence(self): ds = tf.data.Dataset.from_tensor_slices(TEST_DATA) preprocess_fn = stackoverflow_word_prediction.create_preprocess_fn( client_batch_size=32, client_epochs_per_round=1, max_sequence_length=6, max_elements_per_client=100, vocab=['one', 'must'], num_oov_buckets=1) preprocessed_ds = preprocess_fn(ds) element = next(iter(preprocessed_ds)) # BOS is len(vocab)+2, EOS is len(vocab)+3, pad is 0, OOV is len(vocab)+1 self.assertAllEqual(self.evaluate(element[0]), np.array([[4, 1, 2, 3, 5, 0]]))
def test_preprocess_fn_returns_correct_sequence_oov_buckets(self): ds = tf.data.Dataset.from_tensor_slices(TEST_DATA) preprocess_fn = stackoverflow_word_prediction.create_preprocess_fn( client_batch_size=32, client_epochs_per_round=1, max_sequence_length=6, max_elements_per_client=100, vocab=['one', 'must'], num_oov_buckets=3) preprocessed_ds = preprocess_fn(ds) element = next(iter(preprocessed_ds)) # BOS is len(vocab)+3+1 self.assertEqual(self.evaluate(element[0])[0][0], 6) self.assertEqual(self.evaluate(element[0])[0][1], 1) self.assertEqual(self.evaluate(element[0])[0][2], 2) # OOV is [len(vocab)+1, len(vocab)+2, len(vocab)+3] self.assertIn(self.evaluate(element[0])[0][3], [3, 4, 5]) # EOS is len(vocab)+3+2 self.assertEqual(self.evaluate(element[0])[0][4], 7) # pad is 0 self.assertEqual(self.evaluate(element[0])[0][5], 0)
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)