def dry_run_from_params(params: Params, serialization_dir: str) -> None: prepare_environment(params) vocab_params = params.pop("vocabulary", {}) os.makedirs(serialization_dir, exist_ok=True) vocab_dir = os.path.join(serialization_dir, "vocabulary") if os.path.isdir(vocab_dir) and os.listdir(vocab_dir) is not None: raise ConfigurationError("The 'vocabulary' directory in the provided " "serialization directory is non-empty") all_datasets = datasets_from_params(params) datasets_for_vocab_creation = set( params.pop("datasets_for_vocab_creation", all_datasets)) for dataset in datasets_for_vocab_creation: if dataset not in all_datasets: raise ConfigurationError( f"invalid 'dataset_for_vocab_creation' {dataset}") logger.info( "From dataset instances, %s will be considered for vocabulary creation.", ", ".join(datasets_for_vocab_creation)) instances = [ instance for key, dataset in all_datasets.items() for instance in dataset if key in datasets_for_vocab_creation ] vocab = Vocabulary.from_params(vocab_params, instances) dataset = Batch(instances) dataset.index_instances(vocab) dataset.print_statistics() vocab.print_statistics() logger.info(f"writing the vocabulary to {vocab_dir}.") vocab.save_to_files(vocab_dir) model = Model.from_params(vocab=vocab, params=params.pop('model')) trainer_params = params.pop("trainer") no_grad_regexes = trainer_params.pop("no_grad", ()) for name, parameter in model.named_parameters(): if any(re.search(regex, name) for regex in no_grad_regexes): parameter.requires_grad_(False) frozen_parameter_names, tunable_parameter_names = \ get_frozen_and_tunable_parameter_names(model) stdout_handler = prepare_global_logging(serialization_dir, False) logger.info("Following parameters are Frozen (without gradient):") for name in frozen_parameter_names: logger.info(name) logger.info("Following parameters are Tunable (with gradient):") for name in tunable_parameter_names: logger.info(name) cleanup_global_logging(stdout_handler)
def dry_run_from_params(params: Params, serialization_dir: str) -> None: prepare_environment(params) vocab_params = params.pop("vocabulary", {}) os.makedirs(serialization_dir, exist_ok=True) vocab_dir = os.path.join(serialization_dir, "vocabulary") if os.path.isdir(vocab_dir) and os.listdir(vocab_dir) is not None: raise ConfigurationError("The 'vocabulary' directory in the provided " "serialization directory is non-empty") all_datasets = datasets_from_params(params) datasets_for_vocab_creation = set(params.pop("datasets_for_vocab_creation", all_datasets)) for dataset in datasets_for_vocab_creation: if dataset not in all_datasets: raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {dataset}") logger.info("From dataset instances, %s will be considered for vocabulary creation.", ", ".join(datasets_for_vocab_creation)) instances = [instance for key, dataset in all_datasets.items() for instance in dataset if key in datasets_for_vocab_creation] vocab = Vocabulary.from_params(vocab_params, instances) dataset = Batch(instances) dataset.index_instances(vocab) dataset.print_statistics() vocab.print_statistics() logger.info(f"writing the vocabulary to {vocab_dir}.") vocab.save_to_files(vocab_dir) model = Model.from_params(vocab=vocab, params=params.pop('model')) trainer_params = params.pop("trainer") no_grad_regexes = trainer_params.pop("no_grad", ()) for name, parameter in model.named_parameters(): if any(re.search(regex, name) for regex in no_grad_regexes): parameter.requires_grad_(False) frozen_parameter_names, tunable_parameter_names = \ get_frozen_and_tunable_parameter_names(model) logger.info("Following parameters are Frozen (without gradient):") for name in frozen_parameter_names: logger.info(name) logger.info("Following parameters are Tunable (with gradient):") for name in tunable_parameter_names: logger.info(name)
def dry_run_from_params(params: Params, serialization_dir: str, force: bool = False, cache_directory: str = None, cache_prefix: str = None) -> None: """ :param params: :param serialization_dir: force : ``bool``, optional (default=False) If ``True``, we will overwrite the serialization directory if it already exists. cache_directory : ``str``, optional For caching data pre-processing. See :func:`allennlp.training.util.datasets_from_params`. cache_prefix : ``str``, optional For caching data pre-processing. See :func:`allennlp.training.util.datasets_from_params`. :return: """ if os.path.exists(serialization_dir) and force: shutil.rmtree(serialization_dir) prepare_environment(params) vocab_params = params.pop("vocabulary", {}) os.makedirs(serialization_dir, exist_ok=True) vocab_dir = os.path.join(serialization_dir, "vocabulary") if os.path.isdir(vocab_dir) and os.listdir(vocab_dir) is not None: raise ConfigurationError("The 'vocabulary' directory in the provided " "serialization directory is non-empty") all_datasets = datasets_from_params(params, cache_directory, cache_prefix) datasets_for_vocab_creation = set(params.pop("datasets_for_vocab_creation", all_datasets)) for dataset in datasets_for_vocab_creation: if dataset not in all_datasets: raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {dataset}") logger.info("From dataset instances, %s will be considered for vocabulary creation.", ", ".join(datasets_for_vocab_creation)) instances = [instance for key, dataset in all_datasets.items() for instance in dataset if key in datasets_for_vocab_creation] vocab = Vocabulary.from_params(vocab_params, instances) dataset = Batch(instances) dataset.index_instances(vocab) dataset.print_statistics() vocab.print_statistics() logger.info(f"writing the vocabulary to {vocab_dir}.") vocab.save_to_files(vocab_dir) model = Model.from_params(vocab=vocab, params=params.pop('model')) trainer_params = params.pop("trainer") no_grad_regexes = trainer_params.pop("no_grad", ()) for name, parameter in model.named_parameters(): if any(re.search(regex, name) for regex in no_grad_regexes): parameter.requires_grad_(False) frozen_parameter_names, tunable_parameter_names = \ get_frozen_and_tunable_parameter_names(model) logger.info("Following parameters are Frozen (without gradient):") for name in frozen_parameter_names: logger.info(name) logger.info("Following parameters are Tunable (with gradient):") for name in tunable_parameter_names: logger.info(name)