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configure_finetuning.py
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/
configure_finetuning.py
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# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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.
"""Config controlling hyperparameters for fine-tuning ELECTRA."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow.compat.v1 as tf
class FinetuningConfig(object):
"""Fine-tuning hyperparameters."""
def __init__(self, model_name, data_dir, **kwargs):
# general
self.model_name = model_name
self.debug = False # debug mode for quickly running things
self.log_examples = False # print out some train examples for debugging
self.num_trials = 1 # how many train+eval runs to perform
self.do_train = True # train a model
self.do_eval = True # evaluate the model
self.keep_all_models = True # if False, only keep the last trial's ckpt
# model
self.model_size = "small" # one of "small", "base", or "large"
self.task_names = ["chunk"] # which tasks to learn
# override the default transformer hparams for the provided model size; see
# modeling.BertConfig for the possible hparams and util.training_utils for
# the defaults
self.model_hparam_overrides = (
kwargs["model_hparam_overrides"]
if "model_hparam_overrides" in kwargs else {})
self.embedding_size = None # bert hidden size by default
self.vocab_size = 30522 # number of tokens in the vocabulary
self.do_lower_case = True
# training
self.learning_rate = 1e-4
self.weight_decay_rate = 0.01
self.layerwise_lr_decay = 0.8 # if > 0, the learning rate for a layer is
# lr * lr_decay^(depth - max_depth) i.e.,
# shallower layers have lower learning rates
self.num_train_epochs = 3.0 # passes over the dataset during training
self.warmup_proportion = 0.1 # how much of training to warm up the LR for
self.save_checkpoints_steps = 1000000
self.iterations_per_loop = 1000
self.use_tfrecords_if_existing = True # don't make tfrecords and write them
# to disc if existing ones are found
# writing model outputs to disc
self.write_test_outputs = False # whether to write test set outputs,
# currently supported for GLUE + SQuAD 2.0
self.n_writes_test = 5 # write test set predictions for the first n trials
# sizing
self.max_seq_length = 128
self.train_batch_size = 32
self.eval_batch_size = 32
self.predict_batch_size = 32
self.double_unordered = True # for tasks like paraphrase where sentence
# order doesn't matter, train the model on
# on both sentence orderings for each example
# for qa tasks
self.max_query_length = 64 # max tokens in q as opposed to context
self.doc_stride = 128 # stride when splitting doc into multiple examples
self.n_best_size = 20 # number of predictions per example to save
self.max_answer_length = 30 # filter out answers longer than this length
self.answerable_classifier = True # answerable classifier for SQuAD 2.0
self.answerable_uses_start_logits = True # more advanced answerable
# classifier using predicted start
self.answerable_weight = 0.5 # weight for answerability loss
self.joint_prediction = True # jointly predict the start and end positions
# of the answer span
self.beam_size = 20 # beam size when doing joint predictions
self.qa_na_threshold = -2.75 # threshold for "no answer" when writing SQuAD
# 2.0 test outputs
# TPU settings
self.use_tpu = False
self.num_tpu_cores = 1
self.tpu_job_name = None
self.tpu_name = None # cloud TPU to use for training
self.tpu_zone = None # GCE zone where the Cloud TPU is located in
self.gcp_project = None # project name for the Cloud TPU-enabled project
# default locations of data files
self.data_dir = data_dir
pretrained_model_dir = os.path.join(data_dir, "models", model_name)
self.raw_data_dir = os.path.join(data_dir, "finetuning_data", "{:}").format
self.vocab_file = os.path.join(pretrained_model_dir, "vocab.txt")
if not tf.io.gfile.exists(self.vocab_file):
self.vocab_file = os.path.join(self.data_dir, "vocab.txt")
task_names_str = ",".join(
kwargs["task_names"] if "task_names" in kwargs else self.task_names)
self.init_checkpoint = None if self.debug else pretrained_model_dir
self.model_dir = os.path.join(pretrained_model_dir, "finetuning_models",
task_names_str + "_model")
results_dir = os.path.join(pretrained_model_dir, "results")
self.results_txt = os.path.join(results_dir,
task_names_str + "_results.txt")
self.results_pkl = os.path.join(results_dir,
task_names_str + "_results.pkl")
qa_topdir = os.path.join(results_dir, task_names_str + "_qa")
self.qa_eval_file = os.path.join(qa_topdir, "{:}_eval.json").format
self.qa_preds_file = os.path.join(qa_topdir, "{:}_preds.json").format
self.qa_na_file = os.path.join(qa_topdir, "{:}_null_odds.json").format
self.preprocessed_data_dir = os.path.join(
pretrained_model_dir, "finetuning_tfrecords",
task_names_str + "_tfrecords" + ("-debug" if self.debug else ""))
self.test_predictions = os.path.join(
pretrained_model_dir, "test_predictions",
"{:}_{:}_{:}_predictions.pkl").format
# update defaults with passed-in hyperparameters
self.update(kwargs)
# default hyperparameters for single-task models
if len(self.task_names) == 1:
task_name = self.task_names[0]
if task_name == "rte" or task_name == "sts":
self.num_train_epochs = 10.0
elif "squad" in task_name or "qa" in task_name:
self.max_seq_length = 512
self.num_train_epochs = 2.0
self.write_distill_outputs = False
self.write_test_outputs = False
elif task_name == "chunk":
self.max_seq_length = 256
else:
self.num_train_epochs = 3.0
# default hyperparameters for different model sizes
if self.model_size == "large":
self.learning_rate = 5e-5
self.layerwise_lr_decay = 0.9
elif self.model_size == "small":
self.embedding_size = 128
# debug-mode settings
if self.debug:
self.save_checkpoints_steps = 1000000
self.use_tfrecords_if_existing = False
self.num_trials = 1
self.iterations_per_loop = 1
self.train_batch_size = 32
self.num_train_epochs = 3.0
self.log_examples = True
# passed-in-arguments override (for example) debug-mode defaults
self.update(kwargs)
def update(self, kwargs):
for k, v in kwargs.items():
if k not in self.__dict__:
raise ValueError("Unknown hparam " + k)
self.__dict__[k] = v