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- # coding=utf-8
- # Copyright 2018 The Google AI Language Team 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.
- """Extract pre-computed feature vectors from BERT."""
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import codecs
- import collections
- import json
- import re
-
- import modeling
- import tokenization
- import tensorflow as tf
-
- flags = tf.flags
-
- FLAGS = flags.FLAGS
-
- flags.DEFINE_string("input_file", None, "")
-
- flags.DEFINE_string("output_file", None, "")
-
- flags.DEFINE_string("layers", "-1,-2,-3,-4", "")
-
- 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_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_string(
- "init_checkpoint", None,
- "Initial checkpoint (usually from a pre-trained BERT model).")
-
- flags.DEFINE_string("vocab_file", None,
- "The vocabulary file that the BERT model was trained on.")
-
- flags.DEFINE_bool(
- "do_lower_case", True,
- "Whether to lower case the input text. Should be True for uncased "
- "models and False for cased models.")
-
- flags.DEFINE_integer("batch_size", 32, "Batch size for predictions.")
-
- flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
-
- flags.DEFINE_string("master", None,
- "If using a TPU, the address of the master.")
-
- flags.DEFINE_integer(
- "num_tpu_cores", 8,
- "Only used if `use_tpu` is True. Total number of TPU cores to use.")
-
- flags.DEFINE_bool(
- "use_one_hot_embeddings", False,
- "If True, tf.one_hot will be used for embedding lookups, otherwise "
- "tf.nn.embedding_lookup will be used. On TPUs, this should be True "
- "since it is much faster.")
-
-
- class InputExample(object):
-
- def __init__(self, unique_id, text_a, text_b):
- self.unique_id = unique_id
- self.text_a = text_a
- self.text_b = text_b
-
-
- class InputFeatures(object):
- """A single set of features of data."""
-
- def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
- self.unique_id = unique_id
- self.tokens = tokens
- self.input_ids = input_ids
- self.input_mask = input_mask
- self.input_type_ids = input_type_ids
-
-
- def input_fn_builder(features, seq_length):
- """Creates an `input_fn` closure to be passed to TPUEstimator."""
-
- all_unique_ids = []
- all_input_ids = []
- all_input_mask = []
- all_input_type_ids = []
-
- for feature in features:
- all_unique_ids.append(feature.unique_id)
- all_input_ids.append(feature.input_ids)
- all_input_mask.append(feature.input_mask)
- all_input_type_ids.append(feature.input_type_ids)
-
- def input_fn(params):
- """The actual input function."""
- 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({
- "unique_ids":
- tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32),
- "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),
- "input_type_ids":
- tf.constant(
- all_input_type_ids,
- shape=[num_examples, seq_length],
- dtype=tf.int32),
- })
-
- d = d.batch(batch_size=batch_size, drop_remainder=False)
- return d
-
- return input_fn
-
-
- def model_fn_builder(bert_config, init_checkpoint, layer_indexes, 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."""
-
- unique_ids = features["unique_ids"]
- input_ids = features["input_ids"]
- input_mask = features["input_mask"]
- input_type_ids = features["input_type_ids"]
-
- model = modeling.BertModel(
- config=bert_config,
- is_training=False,
- input_ids=input_ids,
- input_mask=input_mask,
- token_type_ids=input_type_ids,
- use_one_hot_embeddings=use_one_hot_embeddings)
-
- if mode != tf.estimator.ModeKeys.PREDICT:
- raise ValueError("Only PREDICT modes are supported: %s" % (mode))
-
- tvars = tf.trainable_variables()
- scaffold_fn = None
- (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)
-
- all_layers = model.get_all_encoder_layers()
-
- predictions = {
- "unique_id": unique_ids,
- }
-
- for (i, layer_index) in enumerate(layer_indexes):
- predictions["layer_output_%d" % i] = all_layers[layer_index]
-
- output_spec = tf.contrib.tpu.TPUEstimatorSpec(
- mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
- return output_spec
-
- return model_fn
-
-
- def convert_examples_to_features(examples, seq_length, tokenizer):
- """Loads a data file into a list of `InputBatch`s."""
-
- features = []
- for (ex_index, example) in enumerate(examples):
- tokens_a = tokenizer.tokenize(example.text_a)
-
- tokens_b = None
- if example.text_b:
- tokens_b = tokenizer.tokenize(example.text_b)
-
- if tokens_b:
- # Modifies `tokens_a` and `tokens_b` in place so that the total
- # length is less than the specified length.
- # Account for [CLS], [SEP], [SEP] with "- 3"
- _truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)
- else:
- # Account for [CLS] and [SEP] with "- 2"
- if len(tokens_a) > seq_length - 2:
- tokens_a = tokens_a[0:(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 as the "sentence vector". Note that this only makes sense because
- # the entire model is fine-tuned.
- tokens = []
- input_type_ids = []
- tokens.append("[CLS]")
- input_type_ids.append(0)
- for token in tokens_a:
- tokens.append(token)
- input_type_ids.append(0)
- tokens.append("[SEP]")
- input_type_ids.append(0)
-
- if tokens_b:
- for token in tokens_b:
- tokens.append(token)
- input_type_ids.append(1)
- tokens.append("[SEP]")
- input_type_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) < seq_length:
- input_ids.append(0)
- input_mask.append(0)
- input_type_ids.append(0)
-
- assert len(input_ids) == seq_length
- assert len(input_mask) == seq_length
- assert len(input_type_ids) == seq_length
-
- if ex_index < 5:
- tf.logging.info("*** Example ***")
- tf.logging.info("unique_id: %s" % (example.unique_id))
- 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(
- "input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
-
- features.append(
- InputFeatures(
- unique_id=example.unique_id,
- tokens=tokens,
- input_ids=input_ids,
- input_mask=input_mask,
- input_type_ids=input_type_ids))
- return features
-
-
- 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 read_examples(input_file):
- """Read a list of `InputExample`s from an input file."""
- examples = []
- unique_id = 0
- with tf.gfile.GFile(input_file, "r") as reader:
- while True:
- line = tokenization.convert_to_unicode(reader.readline())
- if not line:
- break
- line = line.strip()
- text_a = None
- text_b = None
- m = re.match(r"^(.*) \|\|\| (.*)$", line)
- if m is None:
- text_a = line
- else:
- text_a = m.group(1)
- text_b = m.group(2)
- examples.append(
- InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))
- unique_id += 1
- return examples
-
-
- def main(_):
- tf.logging.set_verbosity(tf.logging.INFO)
-
- layer_indexes = [int(x) for x in FLAGS.layers.split(",")]
-
- bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
-
- tokenizer = tokenization.FullTokenizer(
- vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
-
- is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
- run_config = tf.contrib.tpu.RunConfig(
- master=FLAGS.master,
- tpu_config=tf.contrib.tpu.TPUConfig(
- num_shards=FLAGS.num_tpu_cores,
- per_host_input_for_training=is_per_host))
-
- examples = read_examples(FLAGS.input_file)
-
- features = convert_examples_to_features(
- examples=examples, seq_length=FLAGS.max_seq_length, tokenizer=tokenizer)
-
- unique_id_to_feature = {}
- for feature in features:
- unique_id_to_feature[feature.unique_id] = feature
-
- model_fn = model_fn_builder(
- bert_config=bert_config,
- init_checkpoint=FLAGS.init_checkpoint,
- layer_indexes=layer_indexes,
- use_tpu=FLAGS.use_tpu,
- use_one_hot_embeddings=FLAGS.use_one_hot_embeddings)
-
- # 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,
- predict_batch_size=FLAGS.batch_size)
-
- input_fn = input_fn_builder(
- features=features, seq_length=FLAGS.max_seq_length)
-
- with codecs.getwriter("utf-8")(tf.gfile.Open(FLAGS.output_file,
- "w")) as writer:
- for result in estimator.predict(input_fn, yield_single_examples=True):
- unique_id = int(result["unique_id"])
- feature = unique_id_to_feature[unique_id]
- output_json = collections.OrderedDict()
- output_json["linex_index"] = unique_id
- all_features = []
- for (i, token) in enumerate(feature.tokens):
- all_layers = []
- for (j, layer_index) in enumerate(layer_indexes):
- layer_output = result["layer_output_%d" % j]
- layers = collections.OrderedDict()
- layers["index"] = layer_index
- layers["values"] = [
- round(float(x), 6) for x in layer_output[i:(i + 1)].flat
- ]
- all_layers.append(layers)
- features = collections.OrderedDict()
- features["token"] = token
- features["layers"] = all_layers
- all_features.append(features)
- output_json["features"] = all_features
- writer.write(json.dumps(output_json) + "\n")
-
-
- if __name__ == "__main__":
- flags.mark_flag_as_required("input_file")
- flags.mark_flag_as_required("vocab_file")
- flags.mark_flag_as_required("bert_config_file")
- flags.mark_flag_as_required("init_checkpoint")
- flags.mark_flag_as_required("output_file")
- tf.app.run()
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