420 lines
14 KiB
Python
420 lines
14 KiB
Python
# 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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"""Extract pre-computed feature vectors from 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 codecs
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import collections
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import json
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import re
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import modeling
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import tokenization
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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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flags.DEFINE_string("input_file", None, "")
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flags.DEFINE_string("output_file", None, "")
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flags.DEFINE_string("layers", "-1,-2,-3,-4", "")
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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_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.")
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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_string("vocab_file", None,
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"The vocabulary file that the BERT model was trained on.")
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flags.DEFINE_bool(
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"do_lower_case", True,
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"Whether to lower case the input text. Should be True for uncased "
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"models and False for cased models.")
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flags.DEFINE_integer("batch_size", 32, "Batch size for predictions.")
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flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
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flags.DEFINE_string("master", None,
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"If using a TPU, the address of the master.")
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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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flags.DEFINE_bool(
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"use_one_hot_embeddings", False,
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"If True, tf.one_hot will be used for embedding lookups, otherwise "
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"tf.nn.embedding_lookup will be used. On TPUs, this should be True "
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"since it is much faster.")
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class InputExample(object):
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def __init__(self, unique_id, text_a, text_b):
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self.unique_id = unique_id
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self.text_a = text_a
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self.text_b = text_b
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
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self.unique_id = unique_id
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self.tokens = tokens
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.input_type_ids = input_type_ids
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def input_fn_builder(features, seq_length):
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"""Creates an `input_fn` closure to be passed to TPUEstimator."""
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all_unique_ids = []
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all_input_ids = []
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all_input_mask = []
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all_input_type_ids = []
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for feature in features:
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all_unique_ids.append(feature.unique_id)
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all_input_ids.append(feature.input_ids)
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all_input_mask.append(feature.input_mask)
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all_input_type_ids.append(feature.input_type_ids)
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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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num_examples = len(features)
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# This is for demo purposes and does NOT scale to large data sets. We do
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# not use Dataset.from_generator() because that uses tf.py_func which is
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# not TPU compatible. The right way to load data is with TFRecordReader.
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d = tf.data.Dataset.from_tensor_slices({
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"unique_ids":
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tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32),
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"input_ids":
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tf.constant(
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all_input_ids, shape=[num_examples, seq_length],
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dtype=tf.int32),
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"input_mask":
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tf.constant(
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all_input_mask,
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shape=[num_examples, seq_length],
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dtype=tf.int32),
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"input_type_ids":
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tf.constant(
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all_input_type_ids,
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shape=[num_examples, seq_length],
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dtype=tf.int32),
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})
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d = d.batch(batch_size=batch_size, drop_remainder=False)
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return d
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return input_fn
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def model_fn_builder(bert_config, init_checkpoint, layer_indexes, 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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unique_ids = features["unique_ids"]
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input_ids = features["input_ids"]
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input_mask = features["input_mask"]
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input_type_ids = features["input_type_ids"]
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model = modeling.BertModel(
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config=bert_config,
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is_training=False,
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input_ids=input_ids,
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input_mask=input_mask,
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token_type_ids=input_type_ids,
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use_one_hot_embeddings=use_one_hot_embeddings)
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if mode != tf.estimator.ModeKeys.PREDICT:
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raise ValueError("Only PREDICT modes are supported: %s" % (mode))
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tvars = tf.trainable_variables()
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scaffold_fn = None
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(assignment_map,
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initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(
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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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all_layers = model.get_all_encoder_layers()
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predictions = {
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"unique_id": unique_ids,
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}
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for (i, layer_index) in enumerate(layer_indexes):
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predictions["layer_output_%d" % i] = all_layers[layer_index]
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output_spec = tf.contrib.tpu.TPUEstimatorSpec(
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mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
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return output_spec
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return model_fn
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def convert_examples_to_features(examples, seq_length, tokenizer):
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"""Loads a data file into a list of `InputBatch`s."""
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features = []
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for (ex_index, example) in enumerate(examples):
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tokens_a = tokenizer.tokenize(example.text_a)
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tokens_b = None
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if example.text_b:
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tokens_b = tokenizer.tokenize(example.text_b)
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if tokens_b:
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# Modifies `tokens_a` and `tokens_b` in place so that the total
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# length is less than the specified length.
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# Account for [CLS], [SEP], [SEP] with "- 3"
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_truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)
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else:
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# Account for [CLS] and [SEP] with "- 2"
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if len(tokens_a) > seq_length - 2:
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tokens_a = tokens_a[0:(seq_length - 2)]
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# The convention in BERT is:
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# (a) For sequence pairs:
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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# (b) For single sequences:
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# tokens: [CLS] the dog is hairy . [SEP]
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# type_ids: 0 0 0 0 0 0 0
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#
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# Where "type_ids" are used to indicate whether this is the first
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# sequence or the second sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambiguously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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# used as as the "sentence vector". Note that this only makes sense because
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# the entire model is fine-tuned.
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tokens = []
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input_type_ids = []
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tokens.append("[CLS]")
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input_type_ids.append(0)
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for token in tokens_a:
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tokens.append(token)
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input_type_ids.append(0)
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tokens.append("[SEP]")
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input_type_ids.append(0)
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if tokens_b:
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for token in tokens_b:
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tokens.append(token)
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input_type_ids.append(1)
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tokens.append("[SEP]")
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input_type_ids.append(1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence length.
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while len(input_ids) < seq_length:
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input_ids.append(0)
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input_mask.append(0)
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input_type_ids.append(0)
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assert len(input_ids) == seq_length
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assert len(input_mask) == seq_length
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assert len(input_type_ids) == seq_length
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if ex_index < 5:
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tf.logging.info("*** Example ***")
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tf.logging.info("unique_id: %s" % (example.unique_id))
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tf.logging.info("tokens: %s" % " ".join(
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[tokenization.printable_text(x) for x in tokens]))
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tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
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tf.logging.info(
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"input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
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features.append(
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InputFeatures(
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unique_id=example.unique_id,
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tokens=tokens,
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input_ids=input_ids,
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input_mask=input_mask,
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input_type_ids=input_type_ids))
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return features
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def _truncate_seq_pair(tokens_a, tokens_b, max_length):
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"""Truncates a sequence pair in place to the maximum length."""
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# This is a simple heuristic which will always truncate the longer sequence
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# one token at a time. This makes more sense than truncating an equal percent
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# of tokens from each, since if one sequence is very short then each token
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# that's truncated likely contains more information than a longer sequence.
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while True:
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total_length = len(tokens_a) + len(tokens_b)
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if total_length <= max_length:
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break
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if len(tokens_a) > len(tokens_b):
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tokens_a.pop()
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else:
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tokens_b.pop()
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def read_examples(input_file):
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"""Read a list of `InputExample`s from an input file."""
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examples = []
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unique_id = 0
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with tf.gfile.GFile(input_file, "r") as reader:
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while True:
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line = tokenization.convert_to_unicode(reader.readline())
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if not line:
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break
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line = line.strip()
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text_a = None
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text_b = None
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m = re.match(r"^(.*) \|\|\| (.*)$", line)
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if m is None:
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text_a = line
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else:
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text_a = m.group(1)
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text_b = m.group(2)
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examples.append(
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InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))
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unique_id += 1
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return examples
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def main(_):
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tf.logging.set_verbosity(tf.logging.INFO)
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layer_indexes = [int(x) for x in FLAGS.layers.split(",")]
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bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
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tokenizer = tokenization.FullTokenizer(
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vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
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is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
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run_config = tf.contrib.tpu.RunConfig(
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master=FLAGS.master,
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tpu_config=tf.contrib.tpu.TPUConfig(
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num_shards=FLAGS.num_tpu_cores,
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per_host_input_for_training=is_per_host))
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examples = read_examples(FLAGS.input_file)
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features = convert_examples_to_features(
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examples=examples, seq_length=FLAGS.max_seq_length, tokenizer=tokenizer)
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unique_id_to_feature = {}
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for feature in features:
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unique_id_to_feature[feature.unique_id] = feature
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model_fn = model_fn_builder(
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bert_config=bert_config,
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init_checkpoint=FLAGS.init_checkpoint,
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layer_indexes=layer_indexes,
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use_tpu=FLAGS.use_tpu,
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use_one_hot_embeddings=FLAGS.use_one_hot_embeddings)
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# If TPU is not available, this will fall back to normal Estimator on CPU
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# or GPU.
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estimator = tf.contrib.tpu.TPUEstimator(
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use_tpu=FLAGS.use_tpu,
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model_fn=model_fn,
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config=run_config,
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predict_batch_size=FLAGS.batch_size)
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input_fn = input_fn_builder(
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features=features, seq_length=FLAGS.max_seq_length)
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with codecs.getwriter("utf-8")(tf.gfile.Open(FLAGS.output_file,
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"w")) as writer:
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for result in estimator.predict(input_fn, yield_single_examples=True):
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unique_id = int(result["unique_id"])
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feature = unique_id_to_feature[unique_id]
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output_json = collections.OrderedDict()
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output_json["linex_index"] = unique_id
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all_features = []
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for (i, token) in enumerate(feature.tokens):
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all_layers = []
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for (j, layer_index) in enumerate(layer_indexes):
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layer_output = result["layer_output_%d" % j]
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layers = collections.OrderedDict()
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layers["index"] = layer_index
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layers["values"] = [
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round(float(x), 6) for x in layer_output[i:(i + 1)].flat
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]
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all_layers.append(layers)
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features = collections.OrderedDict()
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features["token"] = token
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features["layers"] = all_layers
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all_features.append(features)
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output_json["features"] = all_features
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writer.write(json.dumps(output_json) + "\n")
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if __name__ == "__main__":
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flags.mark_flag_as_required("input_file")
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flags.mark_flag_as_required("vocab_file")
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flags.mark_flag_as_required("bert_config_file")
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flags.mark_flag_as_required("init_checkpoint")
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flags.mark_flag_as_required("output_file")
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tf.app.run()
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