run_embed.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import csv
import os

import tensorflow as tf

from bert import modeling
from bert import optimization
from bert import tokenization

flags = tf.flags
FLAGS = flags.FLAGS

flags.DEFINE_string("bert_config_file", None, "The config json file corresponding to the pre-trained BERT model.")
flags.DEFINE_string("vocab_file", None, "The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string("output_dir", None, "The output directory where the model checkpoints will be written.")
flags.DEFINE_string("export_dir", None, "The export directory where the saved model will be written.")

flags.DEFINE_string("init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_string("model_type", "sentence", "Model type: e.g. 'sentence', 'token', etc.")
flags.DEFINE_bool("do_lower_case", True, "Whether to lower case the input text. True for uncased models and False for cased models.")

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.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
flags.DEFINE_integer("num_tpu_cores", 8,"Only used if `use_tpu` is True. Total number of TPU cores to use.")
flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

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.")

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.")

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.")

class InputExample(object):
    """A single training/test example for simple sequence classification."""
    def __init__(self,
                 guid,
                 text_a,
                 text_b=None,
                 label=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

class PaddingInputExample(object):
    """Fake example so the num input examples is a multiple of the batch size.
    
    When running eval/predict on the TPU, we need to pad the number of examples
    to be a multiple of the batch size, because the TPU requires a fixed batch
    size. The alternative is to drop the last batch, which is bad because it means
    the entire output data won't be generated.
    
    We use this class instead of `None` because treating `None` as padding
    battches could cause silent errors.
    """

class InputFeatures(object):
    """A single set of features of data."""
    def __init__(self,
                 input_ids,
                 input_mask,
                 segment_ids,
                 label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id

def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""
    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()

def convert_single_example(ex_index,
                           example,
                           label_list,
                           max_seq_length,
                           tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""
    if isinstance(example, PaddingInputExample):
        return InputFeatures(
            input_ids=[0] * max_seq_length,
            input_mask=[0] * max_seq_length,
            segment_ids=[0] * max_seq_length,
            label_id=0)

    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i
    
    tokens_a = tokenizer.tokenize(example.text_a)
    tokens_b = None
    if example.text_b:
        tokens_b = tokenizer.tokenize(example.text_b)
    
    if tokens_b:
        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
    else:
        if len(tokens_a) > max_seq_length - 2:
            tokens_a = tokens_a[0:(max_seq_length - 2)]

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0     0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    
    tokens.append("[CLS]")
    segment_ids.append(0)
    
    for token in tokens_a:
        tokens.append(token)
        segment_ids.append(0)
    
    tokens.append("[SEP]")
    segment_ids.append(0)
    
    if tokens_b:
        for token in tokens_b:
            tokens.append(token)
            segment_ids.append(1)
        
        tokens.append("[SEP]")
        segment_ids.append(1)
    
    input_ids = tokenizer.convert_tokens_to_ids(tokens)
    
    # The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
    input_mask = [1] * len(input_ids)
    
    # Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
    
    label_id = label_map[example.label]
    if ex_index < 5:
        tf.logging.info("*** Example ***")
        tf.logging.info("guid: %s" % (example.guid))
        tf.logging.info("tokens: %s" % " ".join([tokenization.printable_text(x) for x in tokens]))
        tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
        tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
        tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=label_id)
    return feature

def convert_examples_to_features(examples,
                                 label_list,
                                 max_seq_length,
                                 tokenizer):
    """Convert a set of `InputExample`s to a list of `InputFeatures`."""
    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
        
        feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer)
        features.append(feature)
    
    return features

def input_fn_builder(features,
                     seq_length,
                     is_training,
                     drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
    all_input_ids = []
    all_input_mask = []
    all_segment_ids = []
    all_label_ids = []
    
    for feature in features:
        all_input_ids.append(feature.input_ids)
        all_input_mask.append(feature.input_mask)
        all_segment_ids.append(feature.segment_ids)
        all_label_ids.append(feature.label_id)
    
    def input_fn(params):
        batch_size = params["batch_size"]
        num_examples = len(features)

        # This is for demo purposes and does NOT scale to large data sets. We do
        # not use Dataset.from_generator() because that uses tf.py_func which is
        # not TPU compatible. The right way to load data is with TFRecordReader.
        d = tf.data.Dataset.from_tensor_slices({
            "input_ids": tf.constant(all_input_ids, shape=[num_examples, seq_length], dtype=tf.int32),
            "input_mask": tf.constant(all_input_mask, shape=[num_examples, seq_length], dtype=tf.int32),
            "segment_ids": tf.constant(all_segment_ids, shape=[num_examples, seq_length], dtype=tf.int32),
            "label_ids": tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
        })
        
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)
        
        d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
        return d
    
    return input_fn

def file_based_convert_examples_to_features(examples,
                                            label_list,
                                            max_seq_length,
                                            tokenizer,
                                            output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""
    def create_int_feature(values):
        return tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
    
    writer = tf.python_io.TFRecordWriter(output_file)
    
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
    
        feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer)
        
        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["label_ids"] = create_int_feature([feature.label_id])
        
        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        
        writer.write(tf_example.SerializeToString())
    
    writer.close()

def file_based_input_fn_builder(input_file,
                                seq_length,
                                is_training,
                                drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "label_ids": tf.FixedLenFeature([], tf.int64),
    }
    
    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 input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]
        
        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        d = tf.data.TFRecordDataset(input_file)
        
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)
        
        d = d.apply(tf.contrib.data.map_and_batch(
            lambda record: _decode_record(record, name_to_features),
            batch_size=batch_size,
            drop_remainder=drop_remainder))
        
        return d
    
    return input_fn

def create_model(bert_config,
                 input_ids,
                 input_mask,
                 segment_ids,
                 use_one_hot_embeddings,
                 model_type):
    """Creates a embedding model."""
    model = modeling.BertModel(
        config=bert_config,
        is_training=False,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings)
    
    # If you want to use sentence-level output, use model.get_pooled_output()
    # If you want to use token-level output, use model.get_sequence_output()
    if model_type == "token":
        output_result = model.get_sequence_output()
    else:
        output_result = model.get_pooled_output()

    return output_result

def model_fn_builder(bert_config,
                     init_checkpoint,
                     use_tpu,
                     use_one_hot_embeddings,
                     model_type):
    """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"]
        
        embeddings = create_model(bert_config, input_ids, input_mask, segment_ids, use_one_hot_embeddings, model_type)
        
        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:
            global_step = tf.train.get_global_step()
            loss = global_step
            train_op = optimization.create_optimizer(global_step, 0.001, 0, 0, use_tpu)
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)
        else:
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                predictions={ "embeddings": embeddings },
                scaffold_fn=scaffold_fn)
        
        return output_spec
    
    return model_fn

def serving_input_fn():
    with tf.variable_scope("export"):
        features = {
            'input_ids': tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_ids'),
            'input_mask': tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_mask'),
            'segment_ids': tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='segment_ids')
        }
        
        return tf.estimator.export.build_raw_serving_input_receiver_fn(features)()

def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)
    
    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
    
    if FLAGS.max_seq_length > bert_config.max_position_embeddings:
        raise ValueError("Cannot use sequence length %d because the BERT model was only trained up to sequence length %d" %
            (FLAGS.max_seq_length, bert_config.max_position_embeddings))
    
    tf.gfile.MakeDirs(FLAGS.output_dir)
    
    tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, FLAGS.init_checkpoint)
    tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
    
    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=1000,
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=1000,
            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,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_tpu,
        model_type=FLAGS.model_type)
    
    # 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,
        export_to_tpu=FLAGS.use_tpu,
        train_batch_size=1,
        eval_batch_size=1,
        predict_batch_size=1)
    
    features = convert_examples_to_features(
        examples=[PaddingInputExample()],
        label_list=["1","0"],
        max_seq_length=FLAGS.max_seq_length,
        tokenizer=tokenizer)
    
    input_fn = input_fn_builder(
        features=features,
        seq_length=FLAGS.max_seq_length,
        is_training=True,
        drop_remainder=True)
    
    estimator.train(input_fn=input_fn, max_steps=1)
    
    tf.gfile.MakeDirs(FLAGS.export_dir)
    estimator.export_savedmodel(FLAGS.export_dir, serving_input_fn, as_text=False)

if __name__ == "__main__":
    flags.mark_flag_as_required("bert_config_file")
    flags.mark_flag_as_required("vocab_file")
    flags.mark_flag_as_required("init_checkpoint")
    flags.mark_flag_as_required("output_dir")
    flags.mark_flag_as_required("export_dir")
    tf.app.run()