494 lines
18 KiB
Python
494 lines
18 KiB
Python
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Run masked LM/next sentence masked_lm pre-training for BERT."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import modeling
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import optimization
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import tensorflow as tf
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flags = tf.flags
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FLAGS = flags.FLAGS
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## Required parameters
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flags.DEFINE_string(
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"bert_config_file", None,
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"The config json file corresponding to the pre-trained BERT model. "
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"This specifies the model architecture.")
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flags.DEFINE_string(
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"input_file", None,
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"Input TF example files (can be a glob or comma separated).")
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flags.DEFINE_string(
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"output_dir", None,
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"The output directory where the model checkpoints will be written.")
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## Other parameters
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flags.DEFINE_string(
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"init_checkpoint", None,
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"Initial checkpoint (usually from a pre-trained BERT model).")
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flags.DEFINE_integer(
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"max_seq_length", 128,
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"The maximum total input sequence length after WordPiece tokenization. "
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"Sequences longer than this will be truncated, and sequences shorter "
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"than this will be padded. Must match data generation.")
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flags.DEFINE_integer(
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"max_predictions_per_seq", 20,
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"Maximum number of masked LM predictions per sequence. "
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"Must match data generation.")
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flags.DEFINE_bool("do_train", False, "Whether to run training.")
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flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
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flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
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flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
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flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
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flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
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flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")
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flags.DEFINE_integer("save_checkpoints_steps", 1000,
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"How often to save the model checkpoint.")
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flags.DEFINE_integer("iterations_per_loop", 1000,
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"How many steps to make in each estimator call.")
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flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.")
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flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
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tf.flags.DEFINE_string(
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"tpu_name", None,
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"The Cloud TPU to use for training. This should be either the name "
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"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
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"url.")
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tf.flags.DEFINE_string(
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"tpu_zone", None,
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"[Optional] GCE zone where the Cloud TPU is located in. If not "
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"specified, we will attempt to automatically detect the GCE project from "
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"metadata.")
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tf.flags.DEFINE_string(
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"gcp_project", None,
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"[Optional] Project name for the Cloud TPU-enabled project. If not "
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"specified, we will attempt to automatically detect the GCE project from "
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"metadata.")
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tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
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flags.DEFINE_integer(
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"num_tpu_cores", 8,
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"Only used if `use_tpu` is True. Total number of TPU cores to use.")
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def model_fn_builder(bert_config, init_checkpoint, learning_rate,
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num_train_steps, num_warmup_steps, use_tpu,
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use_one_hot_embeddings):
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"""Returns `model_fn` closure for TPUEstimator."""
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def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
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"""The `model_fn` for TPUEstimator."""
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tf.logging.info("*** Features ***")
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for name in sorted(features.keys()):
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tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
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input_ids = features["input_ids"]
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input_mask = features["input_mask"]
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segment_ids = features["segment_ids"]
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masked_lm_positions = features["masked_lm_positions"]
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masked_lm_ids = features["masked_lm_ids"]
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masked_lm_weights = features["masked_lm_weights"]
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next_sentence_labels = features["next_sentence_labels"]
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is_training = (mode == tf.estimator.ModeKeys.TRAIN)
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model = modeling.BertModel(
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config=bert_config,
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is_training=is_training,
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input_ids=input_ids,
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input_mask=input_mask,
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token_type_ids=segment_ids,
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use_one_hot_embeddings=use_one_hot_embeddings)
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(masked_lm_loss,
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masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
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bert_config, model.get_sequence_output(), model.get_embedding_table(),
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masked_lm_positions, masked_lm_ids, masked_lm_weights)
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(next_sentence_loss, next_sentence_example_loss,
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next_sentence_log_probs) = get_next_sentence_output(
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bert_config, model.get_pooled_output(), next_sentence_labels)
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total_loss = masked_lm_loss + next_sentence_loss
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tvars = tf.trainable_variables()
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initialized_variable_names = {}
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scaffold_fn = None
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if init_checkpoint:
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(assignment_map, initialized_variable_names
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) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
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if use_tpu:
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def tpu_scaffold():
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tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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return tf.train.Scaffold()
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scaffold_fn = tpu_scaffold
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else:
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tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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tf.logging.info("**** Trainable Variables ****")
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for var in tvars:
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init_string = ""
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if var.name in initialized_variable_names:
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init_string = ", *INIT_FROM_CKPT*"
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tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
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init_string)
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output_spec = None
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if mode == tf.estimator.ModeKeys.TRAIN:
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train_op = optimization.create_optimizer(
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total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
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output_spec = tf.contrib.tpu.TPUEstimatorSpec(
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mode=mode,
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loss=total_loss,
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train_op=train_op,
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scaffold_fn=scaffold_fn)
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elif mode == tf.estimator.ModeKeys.EVAL:
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def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
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masked_lm_weights, next_sentence_example_loss,
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next_sentence_log_probs, next_sentence_labels):
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"""Computes the loss and accuracy of the model."""
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masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
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[-1, masked_lm_log_probs.shape[-1]])
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masked_lm_predictions = tf.argmax(
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masked_lm_log_probs, axis=-1, output_type=tf.int32)
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masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
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masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
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masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
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masked_lm_accuracy = tf.metrics.accuracy(
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labels=masked_lm_ids,
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predictions=masked_lm_predictions,
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weights=masked_lm_weights)
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masked_lm_mean_loss = tf.metrics.mean(
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values=masked_lm_example_loss, weights=masked_lm_weights)
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next_sentence_log_probs = tf.reshape(
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next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
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next_sentence_predictions = tf.argmax(
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next_sentence_log_probs, axis=-1, output_type=tf.int32)
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next_sentence_labels = tf.reshape(next_sentence_labels, [-1])
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next_sentence_accuracy = tf.metrics.accuracy(
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labels=next_sentence_labels, predictions=next_sentence_predictions)
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next_sentence_mean_loss = tf.metrics.mean(
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values=next_sentence_example_loss)
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return {
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"masked_lm_accuracy": masked_lm_accuracy,
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"masked_lm_loss": masked_lm_mean_loss,
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"next_sentence_accuracy": next_sentence_accuracy,
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"next_sentence_loss": next_sentence_mean_loss,
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}
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eval_metrics = (metric_fn, [
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masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
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masked_lm_weights, next_sentence_example_loss,
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next_sentence_log_probs, next_sentence_labels
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])
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output_spec = tf.contrib.tpu.TPUEstimatorSpec(
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mode=mode,
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loss=total_loss,
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eval_metrics=eval_metrics,
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scaffold_fn=scaffold_fn)
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else:
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raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
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return output_spec
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return model_fn
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def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
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label_ids, label_weights):
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"""Get loss and log probs for the masked LM."""
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input_tensor = gather_indexes(input_tensor, positions)
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with tf.variable_scope("cls/predictions"):
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# We apply one more non-linear transformation before the output layer.
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# This matrix is not used after pre-training.
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with tf.variable_scope("transform"):
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input_tensor = tf.layers.dense(
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input_tensor,
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units=bert_config.hidden_size,
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activation=modeling.get_activation(bert_config.hidden_act),
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kernel_initializer=modeling.create_initializer(
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bert_config.initializer_range))
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input_tensor = modeling.layer_norm(input_tensor)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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output_bias = tf.get_variable(
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"output_bias",
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shape=[bert_config.vocab_size],
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initializer=tf.zeros_initializer())
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logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias)
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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label_ids = tf.reshape(label_ids, [-1])
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label_weights = tf.reshape(label_weights, [-1])
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one_hot_labels = tf.one_hot(
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label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
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# The `positions` tensor might be zero-padded (if the sequence is too
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# short to have the maximum number of predictions). The `label_weights`
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# tensor has a value of 1.0 for every real prediction and 0.0 for the
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# padding predictions.
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per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
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numerator = tf.reduce_sum(label_weights * per_example_loss)
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denominator = tf.reduce_sum(label_weights) + 1e-5
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loss = numerator / denominator
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return (loss, per_example_loss, log_probs)
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def get_next_sentence_output(bert_config, input_tensor, labels):
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"""Get loss and log probs for the next sentence prediction."""
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# Simple binary classification. Note that 0 is "next sentence" and 1 is
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# "random sentence". This weight matrix is not used after pre-training.
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with tf.variable_scope("cls/seq_relationship"):
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output_weights = tf.get_variable(
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"output_weights",
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shape=[2, bert_config.hidden_size],
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initializer=modeling.create_initializer(bert_config.initializer_range))
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output_bias = tf.get_variable(
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"output_bias", shape=[2], initializer=tf.zeros_initializer())
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logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias)
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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labels = tf.reshape(labels, [-1])
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one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
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per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
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loss = tf.reduce_mean(per_example_loss)
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return (loss, per_example_loss, log_probs)
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def gather_indexes(sequence_tensor, positions):
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"""Gathers the vectors at the specific positions over a minibatch."""
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sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
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batch_size = sequence_shape[0]
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seq_length = sequence_shape[1]
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width = sequence_shape[2]
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flat_offsets = tf.reshape(
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tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
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flat_positions = tf.reshape(positions + flat_offsets, [-1])
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flat_sequence_tensor = tf.reshape(sequence_tensor,
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[batch_size * seq_length, width])
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output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
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return output_tensor
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def input_fn_builder(input_files,
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max_seq_length,
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max_predictions_per_seq,
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is_training,
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num_cpu_threads=4):
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"""Creates an `input_fn` closure to be passed to TPUEstimator."""
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def input_fn(params):
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"""The actual input function."""
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batch_size = params["batch_size"]
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name_to_features = {
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"input_ids":
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tf.FixedLenFeature([max_seq_length], tf.int64),
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"input_mask":
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tf.FixedLenFeature([max_seq_length], tf.int64),
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"segment_ids":
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tf.FixedLenFeature([max_seq_length], tf.int64),
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"masked_lm_positions":
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tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
|
||
|
|
"masked_lm_ids":
|
||
|
|
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
|
||
|
|
"masked_lm_weights":
|
||
|
|
tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
|
||
|
|
"next_sentence_labels":
|
||
|
|
tf.FixedLenFeature([1], tf.int64),
|
||
|
|
}
|
||
|
|
|
||
|
|
# For training, we want a lot of parallel reading and shuffling.
|
||
|
|
# For eval, we want no shuffling and parallel reading doesn't matter.
|
||
|
|
if is_training:
|
||
|
|
d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
|
||
|
|
d = d.repeat()
|
||
|
|
d = d.shuffle(buffer_size=len(input_files))
|
||
|
|
|
||
|
|
# `cycle_length` is the number of parallel files that get read.
|
||
|
|
cycle_length = min(num_cpu_threads, len(input_files))
|
||
|
|
|
||
|
|
# `sloppy` mode means that the interleaving is not exact. This adds
|
||
|
|
# even more randomness to the training pipeline.
|
||
|
|
d = d.apply(
|
||
|
|
tf.contrib.data.parallel_interleave(
|
||
|
|
tf.data.TFRecordDataset,
|
||
|
|
sloppy=is_training,
|
||
|
|
cycle_length=cycle_length))
|
||
|
|
d = d.shuffle(buffer_size=100)
|
||
|
|
else:
|
||
|
|
d = tf.data.TFRecordDataset(input_files)
|
||
|
|
# Since we evaluate for a fixed number of steps we don't want to encounter
|
||
|
|
# out-of-range exceptions.
|
||
|
|
d = d.repeat()
|
||
|
|
|
||
|
|
# We must `drop_remainder` on training because the TPU requires fixed
|
||
|
|
# size dimensions. For eval, we assume we are evaluating on the CPU or GPU
|
||
|
|
# and we *don't* want to drop the remainder, otherwise we wont cover
|
||
|
|
# every sample.
|
||
|
|
d = d.apply(
|
||
|
|
tf.contrib.data.map_and_batch(
|
||
|
|
lambda record: _decode_record(record, name_to_features),
|
||
|
|
batch_size=batch_size,
|
||
|
|
num_parallel_batches=num_cpu_threads,
|
||
|
|
drop_remainder=True))
|
||
|
|
return d
|
||
|
|
|
||
|
|
return input_fn
|
||
|
|
|
||
|
|
|
||
|
|
def _decode_record(record, name_to_features):
|
||
|
|
"""Decodes a record to a TensorFlow example."""
|
||
|
|
example = tf.parse_single_example(record, name_to_features)
|
||
|
|
|
||
|
|
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
|
||
|
|
# So cast all int64 to int32.
|
||
|
|
for name in list(example.keys()):
|
||
|
|
t = example[name]
|
||
|
|
if t.dtype == tf.int64:
|
||
|
|
t = tf.to_int32(t)
|
||
|
|
example[name] = t
|
||
|
|
|
||
|
|
return example
|
||
|
|
|
||
|
|
|
||
|
|
def main(_):
|
||
|
|
tf.logging.set_verbosity(tf.logging.INFO)
|
||
|
|
|
||
|
|
if not FLAGS.do_train and not FLAGS.do_eval:
|
||
|
|
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
|
||
|
|
|
||
|
|
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
|
||
|
|
|
||
|
|
tf.gfile.MakeDirs(FLAGS.output_dir)
|
||
|
|
|
||
|
|
input_files = []
|
||
|
|
for input_pattern in FLAGS.input_file.split(","):
|
||
|
|
input_files.extend(tf.gfile.Glob(input_pattern))
|
||
|
|
|
||
|
|
tf.logging.info("*** Input Files ***")
|
||
|
|
for input_file in input_files:
|
||
|
|
tf.logging.info(" %s" % input_file)
|
||
|
|
|
||
|
|
tpu_cluster_resolver = None
|
||
|
|
if FLAGS.use_tpu and FLAGS.tpu_name:
|
||
|
|
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
|
||
|
|
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
|
||
|
|
|
||
|
|
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
|
||
|
|
run_config = tf.contrib.tpu.RunConfig(
|
||
|
|
cluster=tpu_cluster_resolver,
|
||
|
|
master=FLAGS.master,
|
||
|
|
model_dir=FLAGS.output_dir,
|
||
|
|
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
|
||
|
|
tpu_config=tf.contrib.tpu.TPUConfig(
|
||
|
|
iterations_per_loop=FLAGS.iterations_per_loop,
|
||
|
|
num_shards=FLAGS.num_tpu_cores,
|
||
|
|
per_host_input_for_training=is_per_host))
|
||
|
|
|
||
|
|
model_fn = model_fn_builder(
|
||
|
|
bert_config=bert_config,
|
||
|
|
init_checkpoint=FLAGS.init_checkpoint,
|
||
|
|
learning_rate=FLAGS.learning_rate,
|
||
|
|
num_train_steps=FLAGS.num_train_steps,
|
||
|
|
num_warmup_steps=FLAGS.num_warmup_steps,
|
||
|
|
use_tpu=FLAGS.use_tpu,
|
||
|
|
use_one_hot_embeddings=FLAGS.use_tpu)
|
||
|
|
|
||
|
|
# If TPU is not available, this will fall back to normal Estimator on CPU
|
||
|
|
# or GPU.
|
||
|
|
estimator = tf.contrib.tpu.TPUEstimator(
|
||
|
|
use_tpu=FLAGS.use_tpu,
|
||
|
|
model_fn=model_fn,
|
||
|
|
config=run_config,
|
||
|
|
train_batch_size=FLAGS.train_batch_size,
|
||
|
|
eval_batch_size=FLAGS.eval_batch_size)
|
||
|
|
|
||
|
|
if FLAGS.do_train:
|
||
|
|
tf.logging.info("***** Running training *****")
|
||
|
|
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
|
||
|
|
train_input_fn = input_fn_builder(
|
||
|
|
input_files=input_files,
|
||
|
|
max_seq_length=FLAGS.max_seq_length,
|
||
|
|
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
|
||
|
|
is_training=True)
|
||
|
|
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
|
||
|
|
|
||
|
|
if FLAGS.do_eval:
|
||
|
|
tf.logging.info("***** Running evaluation *****")
|
||
|
|
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
|
||
|
|
|
||
|
|
eval_input_fn = input_fn_builder(
|
||
|
|
input_files=input_files,
|
||
|
|
max_seq_length=FLAGS.max_seq_length,
|
||
|
|
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
|
||
|
|
is_training=False)
|
||
|
|
|
||
|
|
result = estimator.evaluate(
|
||
|
|
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
|
||
|
|
|
||
|
|
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
|
||
|
|
with tf.gfile.GFile(output_eval_file, "w") as writer:
|
||
|
|
tf.logging.info("***** Eval results *****")
|
||
|
|
for key in sorted(result.keys()):
|
||
|
|
tf.logging.info(" %s = %s", key, str(result[key]))
|
||
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
flags.mark_flag_as_required("input_file")
|
||
|
|
flags.mark_flag_as_required("bert_config_file")
|
||
|
|
flags.mark_flag_as_required("output_dir")
|
||
|
|
tf.app.run()
|