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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Common flags for SuperGLUE finetuning binary."""
from typing import Callable
from absl import flags
from absl import logging
def define_flags():
"""Defines flags."""
# ===========================================================================
# SuperGlue binary flags.
# ===========================================================================
flags.DEFINE_enum(
'mode', 'train_eval_and_predict',
['train_eval_and_predict', 'train_eval', 'predict'],
'The mode to run the binary. If `train_eval_and_predict` '
'it will (1) train on the training data and (2) evaluate on '
'the validation data and (3) finally generate predictions '
'on the prediction data; if `train_eval`, it will only '
'run training and evaluation; if `predict`, it will only '
'run prediction using the model in `model_dir`.')
flags.DEFINE_enum('task_name', None, [
'AX-b',
'CB',
'COPA',
'MULTIRC',
'RTE',
'WiC',
'WSC',
'BoolQ',
'ReCoRD',
'AX-g',
], 'The type of SuperGLUE task.')
flags.DEFINE_string('train_input_path', None,
'The file path to the training data.')
flags.DEFINE_string('validation_input_path', None,
'The file path to the evaluation data.')
flags.DEFINE_string('test_input_path', None,
'The file path to the test input data.')
flags.DEFINE_string('test_output_path', None,
'The file path to the test output data.')
flags.DEFINE_string(
'model_dir', '', 'The model directory containing '
'subdirectories for each task. Only needed for "predict" '
'mode. For all other modes, if not provided, a unique '
'directory will be created automatically for each run.')
flags.DEFINE_string(
'input_meta_data_path', None, 'Path to file that contains '
'metadata about input file. It is output by the `create_finetuning_data` '
'binary. Required for all modes except "predict".')
flags.DEFINE_string('init_checkpoint', '',
'Initial checkpoint from a pre-trained BERT model.')
flags.DEFINE_string(
'model_config_file', '', 'The config file specifying the architecture '
'of the pre-trained model. The file can be either a bert_config.json '
'file or `encoders.EncoderConfig` in yaml file.')
flags.DEFINE_string(
'hub_module_url', '', 'TF-Hub path/url to a pretrained model. If '
'specified, `init_checkpoint` and `model_config_file` flag should not be '
'used.')
flags.DEFINE_multi_string('gin_file', None,
'List of paths to the gin config files.')
flags.DEFINE_multi_string(
'gin_params', None, 'Newline separated list of gin parameter bindings.')
flags.DEFINE_multi_string(
'config_file', None, 'This is the advanced usage to specify the '
'`ExperimentConfig` directly. When specified, '
'we will ignore FLAGS related to `ExperimentConfig` such as '
'`train_input_path`, `validation_input_path` and following hparams.')
# ===========================================================================
# Tuning hparams.
# ===========================================================================
flags.DEFINE_integer('global_batch_size', 32,
'Global batch size for train/eval/predict.')
flags.DEFINE_float('learning_rate', 3e-5, 'Initial learning rate.')
flags.DEFINE_integer('num_epoch', 3, 'Number of training epochs.')
flags.DEFINE_float('warmup_ratio', 0.1,
'Proportion of learning rate warmup steps.')
flags.DEFINE_integer('num_eval_per_epoch', 2,
'Number of evaluations to run per epoch.')
def validate_flags(flags_obj: flags.FlagValues, file_exists_fn: Callable[[str],
bool]):
"""Raises ValueError if any flags are misconfigured.
Args:
flags_obj: A `flags.FlagValues` object, usually from `flags.FLAG`.
file_exists_fn: A callable to decide if a file path exists or not.
"""
def _check_path_exists(flag_path, flag_name):
if not file_exists_fn(flag_path):
raise ValueError('Flag `%s` at %s does not exist.' %
(flag_name, flag_path))
def _validate_path(flag_path, flag_name):
if not flag_path:
raise ValueError('Flag `%s` must be provided in mode %s.' %
(flag_name, flags_obj.mode))
_check_path_exists(flag_path, flag_name)
if 'train' in flags_obj.mode:
_validate_path(flags_obj.train_input_path, 'train_input_path')
_validate_path(flags_obj.input_meta_data_path, 'input_meta_data_path')
if flags_obj.gin_file:
for gin_file in flags_obj.gin_file:
_check_path_exists(gin_file, 'gin_file')
if flags_obj.config_file:
for config_file in flags_obj.config_file:
_check_path_exists(config_file, 'config_file')
if 'eval' in flags_obj.mode:
_validate_path(flags_obj.validation_input_path, 'validation_input_path')
if flags_obj.mode == 'predict':
# model_dir is only needed strictly in 'predict' mode.
_validate_path(flags_obj.model_dir, 'model_dir')
if 'predict' in flags_obj.mode:
_validate_path(flags_obj.test_input_path, 'test_input_path')
if not flags_obj.config_file and flags_obj.mode != 'predict':
if flags_obj.hub_module_url:
if flags_obj.init_checkpoint or flags_obj.model_config_file:
raise ValueError(
'When `hub_module_url` is specified, `init_checkpoint` and '
'`model_config_file` should be empty.')
logging.info('Using the pretrained tf.hub from %s',
flags_obj.hub_module_url)
else:
if not (flags_obj.init_checkpoint and flags_obj.model_config_file):
raise ValueError('Both `init_checkpoint` and `model_config_file` '
'should be specified if `config_file` is not '
'specified.')
_validate_path(flags_obj.model_config_file, 'model_config_file')
logging.info(
'Using the pretrained checkpoint from %s and model_config_file from '
'%s.', flags_obj.init_checkpoint, flags_obj.model_config_file)
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