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- # -*- coding: utf-8 -*-
- """
- Created on January 30 2021
-
- The biomedical entity mask-based self-supervised learning moldel in BioERP are similar to the original implementation described in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". Therefore, the code can be downloaded from https://github.com/google-research/bert. But BERT uses a combination of two tasks, i.e,. masked language learning and the consecutive sentences classification. Nevertheless, different from natural language modeling, meta paths do not have a consecutive relationship. Therefore, BioERP does not involve the continuous sentences training. If you want to run the biomedical entity mask task in BioERP, please manually replace the run_pretraining.py from https://github.com/google-research/bert with this file.
- """
-
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import os
- import modeling
- import optimization
- import tensorflow as tf
- import datetime
- import time
-
- time1 = time.time()
-
- flags = tf.flags
-
- FLAGS = flags.FLAGS
-
- ## Required parameters
- flags.DEFINE_string(
- "bert_config_file", None,
- "The config json file corresponding to the pre-trained BERT model. "
- "This specifies the model architecture.")
-
- flags.DEFINE_string(
- "input_file", None,
- "Input TF example files (can be a glob or comma separated).")
-
- flags.DEFINE_string(
- "output_dir", None,
- "The output directory where the model checkpoints will be written.")
-
- ## Other parameters
- flags.DEFINE_string(
- "init_checkpoint", None,
- "Initial checkpoint (usually from a pre-trained BERT model).")
-
- flags.DEFINE_integer(
- "max_seq_length", 128,
- "The maximum total input sequence length after WordPiece tokenization. "
- "Sequences longer than this will be truncated, and sequences shorter "
- "than this will be padded. Must match data generation.")
-
- flags.DEFINE_integer(
- "max_predictions_per_seq", 20,
- "Maximum number of masked LM predictions per sequence. "
- "Must match data generation.")
-
- flags.DEFINE_bool("do_train", False, "Whether to run training.")
-
- flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
-
- flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
-
- flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
-
- flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
-
- flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
-
- flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")
-
- flags.DEFINE_integer("save_checkpoints_steps", 1000,
- "How often to save the model checkpoint.")
-
- flags.DEFINE_integer("iterations_per_loop", 1000,
- "How many steps to make in each estimator call.")
-
- flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.")
-
- flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
-
- tf.flags.DEFINE_string(
- "tpu_name", None,
- "The Cloud TPU to use for training. This should be either the name "
- "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
- "url.")
-
- tf.flags.DEFINE_string(
- "tpu_zone", None,
- "[Optional] GCE zone where the Cloud TPU is located in. If not "
- "specified, we will attempt to automatically detect the GCE project from "
- "metadata.")
-
- tf.flags.DEFINE_string(
- "gcp_project", None,
- "[Optional] Project name for the Cloud TPU-enabled project. If not "
- "specified, we will attempt to automatically detect the GCE project from "
- "metadata.")
-
- tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
-
- flags.DEFINE_integer(
- "num_tpu_cores", 8,
- "Only used if `use_tpu` is True. Total number of TPU cores to use.")
-
-
- def model_fn_builder(bert_config, init_checkpoint, learning_rate,
- num_train_steps, num_warmup_steps, use_tpu,
- use_one_hot_embeddings):
- """Returns `model_fn` closure for TPUEstimator."""
-
- def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
- """The `model_fn` for TPUEstimator."""
-
- tf.logging.info("*** Features ***")
- for name in sorted(features.keys()):
- tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
-
- input_ids = features["input_ids"]
- input_mask = features["input_mask"]
- segment_ids = features["segment_ids"]
- masked_lm_positions = features["masked_lm_positions"]
- masked_lm_ids = features["masked_lm_ids"]
- masked_lm_weights = features["masked_lm_weights"]
-
- is_training = (mode == tf.estimator.ModeKeys.TRAIN)
-
- model = modeling.BertModel(
- config=bert_config,
- is_training=is_training,
- input_ids=input_ids,
- input_mask=input_mask,
- token_type_ids=segment_ids,
- use_one_hot_embeddings=use_one_hot_embeddings)
-
- (masked_lm_loss,
- masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
- bert_config, model.get_sequence_output(), model.get_embedding_table(),
- masked_lm_positions, masked_lm_ids, masked_lm_weights)
-
- total_loss = masked_lm_loss
-
- tvars = tf.trainable_variables()
-
- initialized_variable_names = {}
- scaffold_fn = None
- if init_checkpoint:
- (assignment_map, initialized_variable_names
- ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
- if use_tpu:
-
- def tpu_scaffold():
- tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
- return tf.train.Scaffold()
-
- scaffold_fn = tpu_scaffold
- else:
- tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
-
- tf.logging.info("**** Trainable Variables ****")
- for var in tvars:
- init_string = ""
- if var.name in initialized_variable_names:
- init_string = ", *INIT_FROM_CKPT*"
- tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
- init_string)
-
- output_spec = None
- if mode == tf.estimator.ModeKeys.TRAIN:
- train_op = optimization.create_optimizer(
- total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
-
- output_spec = tf.contrib.tpu.TPUEstimatorSpec(
- mode=mode,
- loss=total_loss,
- train_op=train_op,
- scaffold_fn=scaffold_fn)
- elif mode == tf.estimator.ModeKeys.EVAL:
-
- def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
- masked_lm_weights):
- """Computes the loss and accuracy of the model."""
- masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
- [-1, masked_lm_log_probs.shape[-1]])
- masked_lm_predictions = tf.argmax(
- masked_lm_log_probs, axis=-1, output_type=tf.int32)
- masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
- masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
- masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
- masked_lm_accuracy = tf.metrics.accuracy(
- labels=masked_lm_ids,
- predictions=masked_lm_predictions,
- weights=masked_lm_weights)
- masked_lm_mean_loss = tf.metrics.mean(
- values=masked_lm_example_loss, weights=masked_lm_weights)
-
-
- return {
- "masked_lm_accuracy": masked_lm_accuracy,
- "masked_lm_loss": masked_lm_mean_loss,
-
- }
-
-
- eval_metrics = (metric_fn, [
- masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
- masked_lm_weights])
- output_spec = tf.contrib.tpu.TPUEstimatorSpec(
- mode=mode,
- loss=total_loss,
- eval_metrics=eval_metrics,
- scaffold_fn=scaffold_fn)
- else:
- raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
-
- return output_spec
-
- return model_fn
-
-
- def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
- label_ids, label_weights):
- """Get loss and log probs for the masked LM."""
- input_tensor = gather_indexes(input_tensor, positions)
-
- with tf.variable_scope("cls/predictions"):
- # We apply one more non-linear transformation before the output layer.
- # This matrix is not used after pre-training.
- with tf.variable_scope("transform"):
- input_tensor = tf.layers.dense(
- input_tensor,
- units=bert_config.hidden_size,
- activation=modeling.get_activation(bert_config.hidden_act),
- kernel_initializer=modeling.create_initializer(
- bert_config.initializer_range))
- input_tensor = modeling.layer_norm(input_tensor)
-
- # The output weights are the same as the input embeddings, but there is
- # an output-only bias for each token.
- output_bias = tf.get_variable(
- "output_bias",
- shape=[bert_config.vocab_size],
- initializer=tf.zeros_initializer())
- logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
- logits = tf.nn.bias_add(logits, output_bias)
- log_probs = tf.nn.log_softmax(logits, axis=-1)
-
- label_ids = tf.reshape(label_ids, [-1])
- label_weights = tf.reshape(label_weights, [-1])
-
- one_hot_labels = tf.one_hot(
- label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
-
- # The `positions` tensor might be zero-padded (if the sequence is too
- # short to have the maximum number of predictions). The `label_weights`
- # tensor has a value of 1.0 for every real prediction and 0.0 for the
- # padding predictions.
- per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
- numerator = tf.reduce_sum(label_weights * per_example_loss)
- denominator = tf.reduce_sum(label_weights) + 1e-5
- loss = numerator / denominator
-
- return (loss, per_example_loss, log_probs)
-
-
-
- def gather_indexes(sequence_tensor, positions):
- """Gathers the vectors at the specific positions over a minibatch."""
- sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
- batch_size = sequence_shape[0]
- seq_length = sequence_shape[1]
- width = sequence_shape[2]
-
- flat_offsets = tf.reshape(
- tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
- flat_positions = tf.reshape(positions + flat_offsets, [-1])
- flat_sequence_tensor = tf.reshape(sequence_tensor,
- [batch_size * seq_length, width])
- output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
- return output_tensor
-
-
- def input_fn_builder(input_files,
- max_seq_length,
- max_predictions_per_seq,
- is_training,
- num_cpu_threads=4):
- """Creates an `input_fn` closure to be passed to TPUEstimator."""
-
- def input_fn(params):
- """The actual input function."""
- batch_size = params["batch_size"]
-
- name_to_features = {
- "input_ids":
- tf.FixedLenFeature([max_seq_length], tf.int64),
- "input_mask":
- tf.FixedLenFeature([max_seq_length], tf.int64),
- "segment_ids":
- tf.FixedLenFeature([max_seq_length], tf.int64),
- "masked_lm_positions":
- 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),
- }
-
- # 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])))
-
- embed_train_time = time.time() - time1
- with open("run_time.txt", 'a') as f:
- f.write("MBTE:" + str(embed_train_time) + '\n')
- print("MBTE run time:", embed_train_time)
-
- 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()
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