def roformer_pretraining() -> cfg.ExperimentConfig: """BERT pretraining experiment.""" config = cfg.ExperimentConfig( runtime=cfg.RuntimeConfig(enable_xla=True), task=masked_lm.MaskedLMConfig( model=bert.PretrainerConfig( encoder=encoders.EncoderConfig( type='any', any=roformer.RoformerEncoderConfig()), cls_heads=[ bert.ClsHeadConfig( inner_dim=768, num_classes=2, dropout_rate=0.1, name='next_sentence') ]), train_data=pretrain_dataloader.BertPretrainDataConfig( use_v2_feature_names=True), validation_data=pretrain_dataloader.BertPretrainDataConfig( use_v2_feature_names=True, is_training=False)), trainer=cfg.TrainerConfig( optimizer_config=RoformerOptimizationConfig(), train_steps=1000000), restrictions=[ 'task.train_data.is_training != None', 'task.validation_data.is_training != None' ]) return config
def bert_pretraining() -> cfg.ExperimentConfig: """BERT pretraining experiment.""" config = cfg.ExperimentConfig( task=masked_lm.MaskedLMConfig( train_data=pretrain_dataloader.BertPretrainDataConfig(), validation_data=pretrain_dataloader.BertPretrainDataConfig( is_training=False)), trainer=cfg.TrainerConfig( train_steps=1000000, optimizer_config=optimization.OptimizationConfig({ 'optimizer': { 'type': 'adamw', 'adamw': { 'weight_decay_rate': 0.01, 'exclude_from_weight_decay': ['LayerNorm', 'layer_norm', 'bias'], } }, 'learning_rate': { 'type': 'polynomial', 'polynomial': { 'initial_learning_rate': 1e-4, 'end_learning_rate': 0.0, } }, 'warmup': { 'type': 'polynomial' } })), restrictions=[ 'task.train_data.is_training != None', 'task.validation_data.is_training != None' ]) return config
def setUp(self): super(ProgressiveMaskedLMTest, self).setUp() self.task_config = progressive_masked_lm.ProgMaskedLMConfig( model=bert.PretrainerConfig( encoder=encoders.EncoderConfig( bert=encoders.BertEncoderConfig(vocab_size=30522, num_layers=2)), cls_heads=[ bert.ClsHeadConfig( inner_dim=10, num_classes=2, name="next_sentence") ]), train_data=pretrain_dataloader.BertPretrainDataConfig( input_path="dummy", max_predictions_per_seq=20, seq_length=128, global_batch_size=1), validation_data=pretrain_dataloader.BertPretrainDataConfig( input_path="dummy", max_predictions_per_seq=20, seq_length=128, global_batch_size=1), stage_list=[ progressive_masked_lm.StackingStageConfig( num_layers=1, num_steps=4), progressive_masked_lm.StackingStageConfig( num_layers=2, num_steps=8), ], ) self.exp_config = cfg.ExperimentConfig( task=self.task_config, trainer=prog_trainer_lib.ProgressiveTrainerConfig())
def bert_pretraining() -> cfg.ExperimentConfig: """BERT pretraining experiment.""" config = cfg.ExperimentConfig( task=masked_lm.MaskedLMConfig( train_data=pretrain_dataloader.BertPretrainDataConfig(), validation_data=pretrain_dataloader.BertPretrainDataConfig( is_training=False)), trainer=_TRAINER, restrictions=[ 'task.train_data.is_training != None', 'task.validation_data.is_training != None' ]) return config
def get_exp_config(): """Get ExperimentConfig.""" params = cfg.ExperimentConfig( task=distillation.BertDistillationTaskConfig( train_data=pretrain_dataloader.BertPretrainDataConfig(), validation_data=pretrain_dataloader.BertPretrainDataConfig( is_training=False)), trainer=prog_trainer_lib.ProgressiveTrainerConfig( progressive=distillation.BertDistillationProgressiveConfig(), optimizer_config=optimization_config, train_steps=740000, checkpoint_interval=20000)) return config_override(params, FLAGS)
def teams_pretrain() -> cfg.ExperimentConfig: """TEAMS pretraining.""" config = cfg.ExperimentConfig( task=teams_task.TeamsPretrainTaskConfig( train_data=pretrain_dataloader.BertPretrainDataConfig(), validation_data=pretrain_dataloader.BertPretrainDataConfig( is_training=False)), trainer=cfg.TrainerConfig( optimizer_config=TeamsOptimizationConfig(), train_steps=1000000), restrictions=[ "task.train_data.is_training != None", "task.validation_data.is_training != None" ]) return config
def test_task(self, num_shared_hidden_layers, num_task_agnostic_layers): config = teams_task.TeamsPretrainTaskConfig( model=teams.TeamsPretrainerConfig( generator=encoders.BertEncoderConfig( vocab_size=30522, num_layers=2), discriminator=encoders.BertEncoderConfig( vocab_size=30522, num_layers=2), num_shared_generator_hidden_layers=num_shared_hidden_layers, num_discriminator_task_agnostic_layers=num_task_agnostic_layers, ), train_data=pretrain_dataloader.BertPretrainDataConfig( input_path="dummy", max_predictions_per_seq=20, seq_length=128, global_batch_size=1)) task = teams_task.TeamsPretrainTask(config) model = task.build_model() metrics = task.build_metrics() dataset = task.build_inputs(config.train_data) iterator = iter(dataset) optimizer = tf.keras.optimizers.SGD(lr=0.1) task.train_step(next(iterator), model, optimizer, metrics=metrics) task.validation_step(next(iterator), model, metrics=metrics)
def test_task(self): config = electra_task.ElectraPretrainConfig( model=electra.ElectraPretrainerConfig( generator_encoder=encoders.EncoderConfig( bert=encoders.BertEncoderConfig(vocab_size=30522, num_layers=1)), discriminator_encoder=encoders.EncoderConfig( bert=encoders.BertEncoderConfig(vocab_size=30522, num_layers=1)), num_masked_tokens=20, sequence_length=128, cls_heads=[ bert.ClsHeadConfig(inner_dim=10, num_classes=2, name="next_sentence") ]), train_data=pretrain_dataloader.BertPretrainDataConfig( input_path="dummy", max_predictions_per_seq=20, seq_length=128, global_batch_size=1)) task = electra_task.ElectraPretrainTask(config) model = task.build_model() metrics = task.build_metrics() dataset = task.build_inputs(config.train_data) iterator = iter(dataset) optimizer = tf.keras.optimizers.SGD(lr=0.1) task.train_step(next(iterator), model, optimizer, metrics=metrics) task.validation_step(next(iterator), model, metrics=metrics)
def test_v2_feature_names(self): train_data_path = os.path.join(self.get_temp_dir(), "train.tf_record") seq_length = 128 max_predictions_per_seq = 20 _create_fake_bert_dataset( train_data_path, seq_length, max_predictions_per_seq, use_next_sentence_label=True, use_position_id=False, use_v2_feature_names=True) data_config = pretrain_dataloader.BertPretrainDataConfig( input_path=train_data_path, max_predictions_per_seq=max_predictions_per_seq, seq_length=seq_length, global_batch_size=10, is_training=True, use_next_sentence_label=True, use_position_id=False, use_v2_feature_names=True) dataset = pretrain_dataloader.BertPretrainDataLoader(data_config).load() features = next(iter(dataset)) self.assertIn("input_word_ids", features) self.assertIn("input_mask", features) self.assertIn("input_type_ids", features) self.assertIn("masked_lm_positions", features) self.assertIn("masked_lm_ids", features) self.assertIn("masked_lm_weights", features)
class StackingStageConfig(base_config.Config): num_steps: int = 0 warmup_steps: int = 10000 initial_learning_rate: float = 1e-4 end_learning_rate: float = 0.0 decay_steps: int = 1000000 override_num_layers: Optional[int] = None small_encoder_config: Optional[ ecfg.SmallEncoderConfig] = ecfg.SmallEncoderConfig() override_train_data: Optional[ pretrain_dataloader. BertPretrainDataConfig] = pretrain_dataloader.BertPretrainDataConfig() override_valid_data: Optional[ pretrain_dataloader. BertPretrainDataConfig] = pretrain_dataloader.BertPretrainDataConfig()
def test_load_data(self, use_next_sentence_label, use_position_id): train_data_path = os.path.join(self.get_temp_dir(), "train.tf_record") seq_length = 128 max_predictions_per_seq = 20 _create_fake_dataset(train_data_path, seq_length, max_predictions_per_seq, use_next_sentence_label=use_next_sentence_label, use_position_id=use_position_id) data_config = pretrain_dataloader.BertPretrainDataConfig( input_path=train_data_path, max_predictions_per_seq=max_predictions_per_seq, seq_length=seq_length, global_batch_size=10, is_training=True, use_next_sentence_label=use_next_sentence_label, use_position_id=use_position_id) dataset = pretrain_dataloader.BertPretrainDataLoader( data_config).load() features = next(iter(dataset)) self.assertLen(features, 6 + int(use_next_sentence_label) + int(use_position_id)) self.assertIn("input_word_ids", features) self.assertIn("input_mask", features) self.assertIn("input_type_ids", features) self.assertIn("masked_lm_positions", features) self.assertIn("masked_lm_ids", features) self.assertIn("masked_lm_weights", features) self.assertEqual("next_sentence_labels" in features, use_next_sentence_label) self.assertEqual("position_ids" in features, use_position_id)
def test_task(self): config = masked_lm.MaskedLMConfig( init_checkpoint=self.get_temp_dir(), scale_loss=True, model=bert.PretrainerConfig( encoder=encoders.EncoderConfig( bert=encoders.BertEncoderConfig(vocab_size=30522, num_layers=1)), cls_heads=[ bert.ClsHeadConfig( inner_dim=10, num_classes=2, name="next_sentence") ]), train_data=pretrain_dataloader.BertPretrainDataConfig( input_path="dummy", max_predictions_per_seq=20, seq_length=128, global_batch_size=1)) task = masked_lm.MaskedLMTask(config) model = task.build_model() metrics = task.build_metrics() dataset = task.build_inputs(config.train_data) iterator = iter(dataset) optimizer = tf.keras.optimizers.SGD(lr=0.1) task.train_step(next(iterator), model, optimizer, metrics=metrics) task.validation_step(next(iterator), model, metrics=metrics) # Saves a checkpoint. ckpt = tf.train.Checkpoint(model=model, **model.checkpoint_items) ckpt.save(config.init_checkpoint) task.initialize(model)
def test_task_determinism(self): config = masked_lm.MaskedLMConfig( init_checkpoint=self.get_temp_dir(), scale_loss=True, model=bert.PretrainerConfig( encoder=encoders.EncoderConfig(bert=encoders.BertEncoderConfig( vocab_size=30522, num_layers=1)), cls_heads=[ bert.ClsHeadConfig(inner_dim=10, num_classes=2, name="next_sentence") ]), train_data=pretrain_dataloader.BertPretrainDataConfig( max_predictions_per_seq=20, seq_length=128, global_batch_size=1)) tf.keras.utils.set_random_seed(1) logs1, validation_logs1, weights1 = self._build_and_run_model(config) tf.keras.utils.set_random_seed(1) logs2, validation_logs2, weights2 = self._build_and_run_model(config) self.assertEqual(logs1["loss"], logs2["loss"]) self.assertEqual(validation_logs1["loss"], validation_logs2["loss"]) for weight1, weight2 in zip(weights1, weights2): self.assertAllEqual(weight1, weight2)
class MaskedLMConfig(cfg.TaskConfig): """The model config.""" init_checkpoint: str = '' model: bert.PretrainerConfig = bert.PretrainerConfig( cls_heads=[ bert.ClsHeadConfig(inner_dim=768, num_classes=2, dropout_rate=0.1, name='next_sentence') ], encoder=encoders.EncoderConfig(bert=encoders.BertEncoderConfig())) scale_loss: bool = False train_data: pretrain_dataloader.BertPretrainDataConfig = pretrain_dataloader.BertPretrainDataConfig( ) small_train_data: pretrain_dataloader.BertPretrainDataConfig = pretrain_dataloader.BertPretrainDataConfig( ) validation_data: pretrain_dataloader.BertPretrainDataConfig = pretrain_dataloader.BertPretrainDataConfig( )
def get_exp_config(): """Get ExperimentConfig.""" params = cfg.ExperimentConfig( task=masked_lm.MaskedLMConfig( train_data=pretrain_dataloader.BertPretrainDataConfig(), small_train_data=pretrain_dataloader.BertPretrainDataConfig(), validation_data=pretrain_dataloader.BertPretrainDataConfig( is_training=False)), trainer=prog_trainer_lib.ProgressiveTrainerConfig( progressive=masked_lm.ProgStackingConfig(), optimizer_config=BertOptimizationConfig(), train_steps=1000000), restrictions=[ 'task.train_data.is_training != None', 'task.validation_data.is_training != None' ]) return utils.config_override(params, FLAGS)
def token_drop_bert_pretraining() -> cfg.ExperimentConfig: """BERT pretraining with token dropping.""" config = cfg.ExperimentConfig( runtime=cfg.RuntimeConfig(enable_xla=True), task=masked_lm.TokenDropMaskedLMConfig( model=bert.PretrainerConfig(encoder=encoders.EncoderConfig( any=encoder_config.TokenDropBertEncoderConfig( vocab_size=30522, num_layers=1, token_keep_k=64), type='any')), train_data=pretrain_dataloader.BertPretrainDataConfig(), validation_data=pretrain_dataloader.BertPretrainDataConfig( is_training=False)), trainer=cfg.TrainerConfig( train_steps=1000000, optimizer_config=optimization.OptimizationConfig({ 'optimizer': { 'type': 'adamw', 'adamw': { 'weight_decay_rate': 0.01, 'exclude_from_weight_decay': ['LayerNorm', 'layer_norm', 'bias'], } }, 'learning_rate': { 'type': 'polynomial', 'polynomial': { 'initial_learning_rate': 1e-4, 'end_learning_rate': 0.0, } }, 'warmup': { 'type': 'polynomial' } })), restrictions=[ 'task.train_data.is_training != None', 'task.validation_data.is_training != None' ]) return config
def bert_dynamic() -> cfg.ExperimentConfig: """BERT base with dynamic input sequences. TPU needs to run with tf.data service with round-robin behavior. """ config = cfg.ExperimentConfig( task=masked_lm.MaskedLMConfig( train_data=pretrain_dynamic_dataloader.BertPretrainDataConfig(), validation_data=pretrain_dataloader.BertPretrainDataConfig( is_training=False)), trainer=_TRAINER, restrictions=[ 'task.train_data.is_training != None', 'task.validation_data.is_training != None' ]) return config
def prepare_config(self, teacher_block_num, student_block_num, transfer_teacher_layers): # using small model for testing task_config = distillation.BertDistillationTaskConfig( teacher_model=bert.PretrainerConfig(encoder=encoders.EncoderConfig( type='mobilebert', mobilebert=encoders.MobileBertEncoderConfig( num_blocks=teacher_block_num)), cls_heads=[ bert.ClsHeadConfig( inner_dim=256, num_classes=2, dropout_rate=0.1, name='next_sentence') ], mlm_activation='gelu'), student_model=bert.PretrainerConfig(encoder=encoders.EncoderConfig( type='mobilebert', mobilebert=encoders.MobileBertEncoderConfig( num_blocks=student_block_num)), cls_heads=[ bert.ClsHeadConfig( inner_dim=256, num_classes=2, dropout_rate=0.1, name='next_sentence') ], mlm_activation='relu'), train_data=pretrain_dataloader.BertPretrainDataConfig( input_path='dummy', max_predictions_per_seq=76, seq_length=512, global_batch_size=10), validation_data=pretrain_dataloader.BertPretrainDataConfig( input_path='dummy', max_predictions_per_seq=76, seq_length=512, global_batch_size=10)) # set only 1 step for each stage progressive_config = distillation.BertDistillationProgressiveConfig() progressive_config.layer_wise_distill_config.transfer_teacher_layers = ( transfer_teacher_layers) progressive_config.layer_wise_distill_config.num_steps = 1 progressive_config.pretrain_distill_config.num_steps = 1 optimization_config = optimization.OptimizationConfig( optimizer=optimization.OptimizerConfig( type='lamb', lamb=optimization.LAMBConfig(weight_decay_rate=0.0001, exclude_from_weight_decay=[ 'LayerNorm', 'layer_norm', 'bias', 'no_norm' ])), learning_rate=optimization.LrConfig( type='polynomial', polynomial=optimization.PolynomialLrConfig( initial_learning_rate=1.5e-3, decay_steps=10000, end_learning_rate=1.5e-3)), warmup=optimization.WarmupConfig( type='linear', linear=optimization.LinearWarmupConfig( warmup_learning_rate=0))) exp_config = cfg.ExperimentConfig( task=task_config, trainer=prog_trainer_lib.ProgressiveTrainerConfig( progressive=progressive_config, optimizer_config=optimization_config)) # Create a teacher model checkpoint. teacher_encoder = encoders.build_encoder( task_config.teacher_model.encoder) pretrainer_config = task_config.teacher_model if pretrainer_config.cls_heads: teacher_cls_heads = [ layers.ClassificationHead(**cfg.as_dict()) for cfg in pretrainer_config.cls_heads ] else: teacher_cls_heads = [] masked_lm = layers.MobileBertMaskedLM( embedding_table=teacher_encoder.get_embedding_table(), activation=tf_utils.get_activation( pretrainer_config.mlm_activation), initializer=tf.keras.initializers.TruncatedNormal( stddev=pretrainer_config.mlm_initializer_range), name='cls/predictions') teacher_pretrainer = models.BertPretrainerV2( encoder_network=teacher_encoder, classification_heads=teacher_cls_heads, customized_masked_lm=masked_lm) # The model variables will be created after the forward call. _ = teacher_pretrainer(teacher_pretrainer.inputs) teacher_pretrainer_ckpt = tf.train.Checkpoint( **teacher_pretrainer.checkpoint_items) teacher_ckpt_path = os.path.join(self.get_temp_dir(), 'teacher_model.ckpt') teacher_pretrainer_ckpt.save(teacher_ckpt_path) exp_config.task.teacher_model_init_checkpoint = self.get_temp_dir() return exp_config
def test_distribution_strategy(self, distribution_strategy): max_seq_length = 128 batch_size = 8 input_path = os.path.join(self.get_temp_dir(), 'train.tf_record') _create_fake_dataset(input_path, seq_length=60, num_masked_tokens=20, max_seq_length=max_seq_length, num_examples=batch_size) data_config = pretrain_dynamic_dataloader.BertPretrainDataConfig( is_training=False, input_path=input_path, seq_bucket_lengths=[64, 128], global_batch_size=batch_size) dataloader = pretrain_dynamic_dataloader.PretrainingDynamicDataLoader( data_config) distributed_ds = orbit.utils.make_distributed_dataset( distribution_strategy, dataloader.load) train_iter = iter(distributed_ds) with distribution_strategy.scope(): config = masked_lm.MaskedLMConfig( init_checkpoint=self.get_temp_dir(), model=bert.PretrainerConfig( encoders.EncoderConfig(bert=encoders.BertEncoderConfig( vocab_size=30522, num_layers=1)), cls_heads=[ bert.ClsHeadConfig(inner_dim=10, num_classes=2, name='next_sentence') ]), train_data=data_config) task = masked_lm.MaskedLMTask(config) model = task.build_model() metrics = task.build_metrics() @tf.function def step_fn(features): return task.validation_step(features, model, metrics=metrics) distributed_outputs = distribution_strategy.run( step_fn, args=(next(train_iter), )) local_results = tf.nest.map_structure( distribution_strategy.experimental_local_results, distributed_outputs) logging.info('Dynamic padding: local_results= %s', str(local_results)) dynamic_metrics = {} for metric in metrics: dynamic_metrics[metric.name] = metric.result() data_config = pretrain_dataloader.BertPretrainDataConfig( is_training=False, input_path=input_path, seq_length=max_seq_length, max_predictions_per_seq=20, global_batch_size=batch_size) dataloader = pretrain_dataloader.BertPretrainDataLoader(data_config) distributed_ds = orbit.utils.make_distributed_dataset( distribution_strategy, dataloader.load) train_iter = iter(distributed_ds) with distribution_strategy.scope(): metrics = task.build_metrics() @tf.function def step_fn_b(features): return task.validation_step(features, model, metrics=metrics) distributed_outputs = distribution_strategy.run( step_fn_b, args=(next(train_iter), )) local_results = tf.nest.map_structure( distribution_strategy.experimental_local_results, distributed_outputs) logging.info('Static padding: local_results= %s', str(local_results)) static_metrics = {} for metric in metrics: static_metrics[metric.name] = metric.result() for key in static_metrics: # We need to investigate the differences on losses. if key != 'next_sentence_loss': self.assertEqual(dynamic_metrics[key], static_metrics[key])