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from __gin__ import dynamic_registration |
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import __main__ as train_script |
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import seqio |
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from t5x import adafactor |
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from t5x.examples.t5 import network |
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from t5x import gin_utils |
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from t5x import models |
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from t5x import partitioning |
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from t5x import trainer |
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from t5x import utils |
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import tasks |
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BATCH_SIZE = 256 |
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DROPOUT_RATE = 0.0 |
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LABEL_SMOOTHING = 0.0 |
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LOSS_NORMALIZING_FACTOR = None |
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MIXTURE_OR_TASK_MODULE = None |
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MIXTURE_OR_TASK_NAME = 'pretrain_finnish' |
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MODEL = @models.EncoderDecoderModel() |
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MODEL_DIR = '/researchdisk/t5-small-nl16-finnish' |
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OPTIMIZER = @adafactor.Adafactor() |
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RANDOM_SEED = None |
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SHUFFLE_TRAIN_EXAMPLES = True |
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TASK_FEATURE_LENGTHS = {'inputs': 512, 'targets': 512} |
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TRAIN_STEPS = 500000 |
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USE_CACHED_TASKS = False |
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USE_HARDWARE_RNG = False |
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VOCABULARY = @seqio.SentencePieceVocabulary() |
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Z_LOSS = 0.0001 |
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# Parameters for adafactor.Adafactor: |
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# ============================================================================== |
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adafactor.Adafactor.decay_rate = 0.8 |
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adafactor.Adafactor.logical_factor_rules = \ |
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@adafactor.standard_logical_factor_rules() |
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adafactor.Adafactor.step_offset = 0 |
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# Parameters for utils.CheckpointConfig: |
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# ============================================================================== |
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utils.CheckpointConfig.restore = @utils.RestoreCheckpointConfig() |
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utils.CheckpointConfig.save = @utils.SaveCheckpointConfig() |
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# Parameters for utils.create_learning_rate_scheduler: |
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# ============================================================================== |
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utils.create_learning_rate_scheduler.base_learning_rate = 1.0 |
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utils.create_learning_rate_scheduler.factors = 'constant * rsqrt_decay' |
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utils.create_learning_rate_scheduler.warmup_steps = 10000 |
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# Parameters for train/utils.DatasetConfig: |
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# ============================================================================== |
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train/utils.DatasetConfig.batch_size = %BATCH_SIZE |
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train/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME |
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train/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE |
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train/utils.DatasetConfig.pack = True |
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train/utils.DatasetConfig.seed = None |
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train/utils.DatasetConfig.shuffle = %SHUFFLE_TRAIN_EXAMPLES |
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train/utils.DatasetConfig.split = 'train' |
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train/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS |
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train/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS |
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# Parameters for train_eval/utils.DatasetConfig: |
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# ============================================================================== |
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train_eval/utils.DatasetConfig.batch_size = %BATCH_SIZE |
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train_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME |
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train_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE |
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train_eval/utils.DatasetConfig.pack = True |
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train_eval/utils.DatasetConfig.seed = 42 |
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train_eval/utils.DatasetConfig.shuffle = False |
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train_eval/utils.DatasetConfig.split = 'validation' |
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train_eval/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS |
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train_eval/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS |
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# Parameters for models.EncoderDecoderModel: |
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# ============================================================================== |
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models.EncoderDecoderModel.input_vocabulary = %VOCABULARY |
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models.EncoderDecoderModel.label_smoothing = %LABEL_SMOOTHING |
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models.EncoderDecoderModel.loss_normalizing_factor = %LOSS_NORMALIZING_FACTOR |
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models.EncoderDecoderModel.module = @network.Transformer() |
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models.EncoderDecoderModel.optimizer_def = %OPTIMIZER |
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models.EncoderDecoderModel.output_vocabulary = %VOCABULARY |
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models.EncoderDecoderModel.z_loss = %Z_LOSS |
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# Parameters for partitioning.PjitPartitioner: |
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# ============================================================================== |
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partitioning.PjitPartitioner.logical_axis_rules = \ |
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@partitioning.standard_logical_axis_rules() |
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partitioning.PjitPartitioner.model_parallel_submesh = None |
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partitioning.PjitPartitioner.num_partitions = 1 |
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# Parameters for utils.RestoreCheckpointConfig: |
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# ============================================================================== |
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utils.RestoreCheckpointConfig.path = [] |
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# Parameters for utils.SaveCheckpointConfig: |
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# ============================================================================== |
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utils.SaveCheckpointConfig.dtype = 'float32' |
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utils.SaveCheckpointConfig.keep = 10 |
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utils.SaveCheckpointConfig.period = 10000 |
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utils.SaveCheckpointConfig.save_dataset = False |
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# Parameters for seqio.SentencePieceVocabulary: |
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# ============================================================================== |
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seqio.SentencePieceVocabulary.sentencepiece_model_file = 'spiece.model' |
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# Parameters for network.T5Config: |
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# ============================================================================== |
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network.T5Config.dropout_rate = %DROPOUT_RATE |
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network.T5Config.dtype = 'bfloat16' |
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network.T5Config.emb_dim = 512 |
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network.T5Config.head_dim = 64 |
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network.T5Config.logits_via_embedding = False |
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network.T5Config.mlp_activations = ('gelu', 'linear') |
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network.T5Config.mlp_dim = 2048 |
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network.T5Config.num_decoder_layers = 16 |
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network.T5Config.num_encoder_layers = 16 |
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network.T5Config.num_heads = 8 |
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network.T5Config.vocab_size = 32128 |
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# Parameters for train_script.train: |
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# ============================================================================== |
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train_script.train.checkpoint_cfg = @utils.CheckpointConfig() |
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train_script.train.eval_period = 10000 |
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train_script.train.eval_steps = 20 |
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train_script.train.infer_eval_dataset_cfg = None |
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train_script.train.model = %MODEL |
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train_script.train.model_dir = %MODEL_DIR |
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train_script.train.partitioner = @partitioning.PjitPartitioner() |
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train_script.train.random_seed = %RANDOM_SEED |
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train_script.train.summarize_config_fn = @gin_utils.summarize_gin_config |
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train_script.train.total_steps = %TRAIN_STEPS |
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train_script.train.train_dataset_cfg = @train/utils.DatasetConfig() |
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train_script.train.train_eval_dataset_cfg = @train_eval/utils.DatasetConfig() |
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train_script.train.trainer_cls = @trainer.Trainer |
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train_script.train.use_hardware_rng = %USE_HARDWARE_RNG |
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# Parameters for trainer.Trainer: |
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# ============================================================================== |
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trainer.Trainer.learning_rate_fn = @utils.create_learning_rate_scheduler() |
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trainer.Trainer.num_microbatches = None |
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# Parameters for network.Transformer: |
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# ============================================================================== |
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network.Transformer.config = @network.T5Config() |
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