def train(self, mixture_or_task_name, steps, init_checkpoint=None, split="train"): """Train the model on the given Mixture or Task. Args: mixture_or_task_name: str, the name of the Mixture or Task to train on. Must be pre-registered in the global `TaskRegistry` or `MixtureRegistry.` steps: int, the total number of steps to train for. init_checkpoint: a string, if not None then read in variables from this checkpoint path when initializing variables. Will only initialize variables that appear both in the current graph and the checkpoint. split: str, the mixture/task split to train on. """ vocabulary = t5.models.mesh_transformer.get_vocabulary( mixture_or_task_name) dataset_fn = functools.partial( t5.models.mesh_transformer.mesh_train_dataset_fn, mixture_or_task_name=mixture_or_task_name, ) mtf_utils.train_model(self.estimator(vocabulary, init_checkpoint), vocabulary, self._sequence_length, self.batch_size, dataset_fn, steps, self._ensemble_inputs, dataset_split=split)
def train(self, mixture_or_task_name, steps, init_checkpoint=None, split="train"): """Train the model on the given Mixture or Task. Args: mixture_or_task_name: str, the name of the Mixture or Task to train on. Must be pre-registered in the global `TaskRegistry` or `MixtureRegistry.` steps: int, the total number of steps to train for. init_checkpoint: a string, if not None then read in variables from this checkpoint path when initializing variables. Will only initialize variables that appear both in the current graph and the checkpoint. """ vocabulary = get_mixture_or_task_ll( mixture_or_task_name).get_vocabulary() dataset_fn = functools.partial( mesh_train_dataset_fn_ll, mixture_or_task_name=mixture_or_task_name, batch_size=self.batch_size, ensemble_inputs=self._ensemble_inputs, group_by_attribute=self.group_by_attribute) # When fine-tuning, we first load the gin config of the pre-trained model. Yet here we might set gin parameters # with different values than the gin parameter values from the pre-trained gin config. e.g. # t5.data.preprocessors.unsupervised.preprocessors. if self.group_by_attribute: train_model_ll(self.estimator(vocabulary, init_checkpoint), vocabulary, self._sequence_length, self.batch_size, dataset_fn, steps, self._ensemble_inputs, dataset_split=split) else: utils.train_model(self.estimator(vocabulary, init_checkpoint), vocabulary, self._sequence_length, self.batch_size, dataset_fn, steps, self._ensemble_inputs, dataset_split=split)
def train(self, mixture_or_task_name, steps): dataset_fn = functools.partial( mesh_train_dataset_fn, mixture_or_task_name=mixture_or_task_name) utils.train_model(self._estimator, self._vocabulary, self._sequence_length, self._batch_size, dataset_fn, steps, self._ensemble_inputs)