Spaces:
Sleeping
Sleeping
File size: 5,720 Bytes
5672777 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
# 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.
"""XLNet pretraining runner in tf2.0."""
import functools
import os
# Import libraries
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf, tf_keras
# pylint: disable=unused-import
from official.common import distribute_utils
from official.legacy.xlnet import common_flags
from official.legacy.xlnet import data_utils
from official.legacy.xlnet import optimization
from official.legacy.xlnet import training_utils
from official.legacy.xlnet import xlnet_config
from official.legacy.xlnet import xlnet_modeling as modeling
flags.DEFINE_integer(
"num_predict",
default=None,
help="Number of tokens to predict in partial prediction.")
# FLAGS for pretrain input preprocessing
flags.DEFINE_integer("perm_size", 0, help="Window size of permutation.")
flags.DEFINE_float("leak_ratio", default=0.1,
help="Percent of masked tokens that are leaked.")
flags.DEFINE_enum("sample_strategy", default="token_span",
enum_values=["single_token", "whole_word", "token_span",
"word_span"],
help="Stragey used to sample prediction targets.")
flags.DEFINE_integer("max_num_tokens", default=5,
help="Maximum number of tokens to sample in a span."
"Effective when token_span strategy is used.")
flags.DEFINE_integer("min_num_tokens", default=1,
help="Minimum number of tokens to sample in a span."
"Effective when token_span strategy is used.")
flags.DEFINE_integer("max_num_words", default=5,
help="Maximum number of whole words to sample in a span."
"Effective when word_span strategy is used.")
flags.DEFINE_integer("min_num_words", default=1,
help="Minimum number of whole words to sample in a span."
"Effective when word_span strategy is used.")
FLAGS = flags.FLAGS
def get_pretrainxlnet_model(model_config, run_config):
return modeling.PretrainingXLNetModel(
use_proj=True,
xlnet_config=model_config,
run_config=run_config,
name="model")
def main(unused_argv):
del unused_argv
num_hosts = 1
strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=FLAGS.strategy_type,
tpu_address=FLAGS.tpu)
if FLAGS.strategy_type == "tpu":
num_hosts = strategy.extended.num_hosts
if strategy:
logging.info("***** Number of cores used : %d",
strategy.num_replicas_in_sync)
logging.info("***** Number of hosts used : %d", num_hosts)
online_masking_config = data_utils.OnlineMaskingConfig(
sample_strategy=FLAGS.sample_strategy,
max_num_tokens=FLAGS.max_num_tokens,
min_num_tokens=FLAGS.min_num_tokens,
max_num_words=FLAGS.max_num_words,
min_num_words=FLAGS.min_num_words)
train_input_fn = functools.partial(
data_utils.get_pretrain_input_data, FLAGS.train_batch_size, FLAGS.seq_len,
strategy, FLAGS.train_tfrecord_path, FLAGS.reuse_len, FLAGS.perm_size,
FLAGS.leak_ratio, FLAGS.num_predict, FLAGS.uncased, online_masking_config,
num_hosts)
total_training_steps = FLAGS.train_steps
steps_per_loop = FLAGS.iterations
optimizer, learning_rate_fn = optimization.create_optimizer(
init_lr=FLAGS.learning_rate,
num_train_steps=total_training_steps,
num_warmup_steps=FLAGS.warmup_steps,
min_lr_ratio=FLAGS.min_lr_ratio,
adam_epsilon=FLAGS.adam_epsilon,
weight_decay_rate=FLAGS.weight_decay_rate)
model_config = xlnet_config.XLNetConfig(FLAGS)
run_config = xlnet_config.create_run_config(True, False, FLAGS)
input_meta_data = {}
input_meta_data["d_model"] = FLAGS.d_model
input_meta_data["mem_len"] = FLAGS.mem_len
input_meta_data["batch_size_per_core"] = int(FLAGS.train_batch_size /
strategy.num_replicas_in_sync)
input_meta_data["n_layer"] = FLAGS.n_layer
input_meta_data["lr_layer_decay_rate"] = FLAGS.lr_layer_decay_rate
model_fn = functools.partial(get_pretrainxlnet_model, model_config,
run_config)
model = training_utils.train(
strategy=strategy,
model_fn=model_fn,
input_meta_data=input_meta_data,
eval_fn=None,
metric_fn=None,
train_input_fn=train_input_fn,
init_checkpoint=FLAGS.init_checkpoint,
init_from_transformerxl=FLAGS.init_from_transformerxl,
total_training_steps=total_training_steps,
steps_per_loop=steps_per_loop,
optimizer=optimizer,
learning_rate_fn=learning_rate_fn,
model_dir=FLAGS.model_dir,
save_steps=FLAGS.save_steps)
# Export transformer-xl model checkpoint to be used in finetuning.
checkpoint = tf.train.Checkpoint(transformer_xl=model.transformerxl_model)
saved_path = checkpoint.save(
os.path.join(FLAGS.model_dir, "pretrained/transformer_xl.ckpt"))
logging.info("Exporting the transformer-xl model as a new TF checkpoint: %s",
saved_path)
if __name__ == "__main__":
app.run(main)
|