1057 lines
38 KiB
Python
1057 lines
38 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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"""BERT finetuning runner."""
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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 collections
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import csv
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import os
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import modeling
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import optimization
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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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## Required parameters
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flags.DEFINE_string(
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"data_dir", None,
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"The input data dir. Should contain the .tsv files (or other data files) "
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"for the task.")
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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("task_name", None, "The name of the task to train.")
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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_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_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(
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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_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_bool(
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"do_predict", False,
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"Whether to run the model in inference mode on the test 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_integer("predict_batch_size", 8, "Total batch size for predict.")
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flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
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flags.DEFINE_float("num_train_epochs", 3.0,
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"Total number of training epochs to perform.")
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flags.DEFINE_float(
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"warmup_proportion", 0.1,
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"Proportion of training to perform linear learning rate warmup for. "
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"E.g., 0.1 = 10% of training.")
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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_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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class InputExample(object):
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"""A single training/test example for simple sequence classification."""
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def __init__(self, guid, text_a, text_b=None, label=None):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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text_a: string. The untokenized text of the first sequence. For single
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sequence tasks, only this sequence must be specified.
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text_b: (Optional) string. The untokenized text of the second sequence.
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Only must be specified for sequence pair tasks.
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label: (Optional) string. The label of the example. This should be
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specified for train and dev examples, but not for test examples.
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"""
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self.guid = guid
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self.text_a = text_a
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self.text_b = text_b
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self.label = label
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class PaddingInputExample(object):
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"""Fake example so the num input examples is a multiple of the batch size.
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When running eval/predict on the TPU, we need to pad the number of examples
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to be a multiple of the batch size, because the TPU requires a fixed batch
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size. The alternative is to drop the last batch, which is bad because it means
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the entire output data won't be generated.
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We use this class instead of `None` because treating `None` as padding
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battches could cause silent errors.
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"""
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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,
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input_ids,
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input_mask,
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segment_ids,
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label_id,
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is_real_example=True):
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.label_id = label_id
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self.is_real_example = is_real_example
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class DataProcessor(object):
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"""Base class for data converters for sequence classification data sets."""
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def get_train_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the train set."""
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raise NotImplementedError()
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def get_dev_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the dev set."""
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raise NotImplementedError()
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def get_test_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for prediction."""
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raise NotImplementedError()
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def get_labels(self):
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"""Gets the list of labels for this data set."""
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raise NotImplementedError()
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@classmethod
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def _read_tsv(cls, input_file, quotechar=None):
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"""Reads a tab separated value file."""
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with tf.gfile.Open(input_file, "r") as f:
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reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
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lines = []
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for line in reader:
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lines.append(line)
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return lines
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class XnliProcessor(DataProcessor):
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"""Processor for the XNLI data set."""
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def __init__(self):
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self.language = "zh"
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def get_train_examples(self, data_dir):
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"""See base class."""
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lines = self._read_tsv(
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os.path.join(data_dir, "multinli",
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"multinli.train.%s.tsv" % self.language))
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "train-%d" % (i)
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text_a = tokenization.convert_to_unicode(line[0])
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text_b = tokenization.convert_to_unicode(line[1])
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label = tokenization.convert_to_unicode(line[2])
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if label == tokenization.convert_to_unicode("contradictory"):
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label = tokenization.convert_to_unicode("contradiction")
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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def get_dev_examples(self, data_dir):
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"""See base class."""
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lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "dev-%d" % (i)
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language = tokenization.convert_to_unicode(line[0])
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if language != tokenization.convert_to_unicode(self.language):
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continue
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text_a = tokenization.convert_to_unicode(line[6])
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text_b = tokenization.convert_to_unicode(line[7])
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label = tokenization.convert_to_unicode(line[1])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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def get_labels(self):
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"""See base class."""
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return ["contradiction", "entailment", "neutral"]
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class MnliProcessor(DataProcessor):
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"""Processor for the MultiNLI data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
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"dev_matched")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["contradiction", "entailment", "neutral"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))
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text_a = tokenization.convert_to_unicode(line[8])
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text_b = tokenization.convert_to_unicode(line[9])
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if set_type == "test":
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label = "contradiction"
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else:
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label = tokenization.convert_to_unicode(line[-1])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class MrpcProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, i)
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text_a = tokenization.convert_to_unicode(line[3])
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text_b = tokenization.convert_to_unicode(line[4])
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if set_type == "test":
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label = "0"
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else:
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label = tokenization.convert_to_unicode(line[0])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class ColaProcessor(DataProcessor):
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"""Processor for the CoLA data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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# Only the test set has a header
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if set_type == "test" and i == 0:
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continue
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guid = "%s-%s" % (set_type, i)
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if set_type == "test":
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text_a = tokenization.convert_to_unicode(line[1])
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label = "0"
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else:
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text_a = tokenization.convert_to_unicode(line[3])
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label = tokenization.convert_to_unicode(line[1])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
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return examples
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import dealing_dataset
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class EPProcessor(DataProcessor):
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"""Processor for the Emotion data set ."""
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def get_train_examples(self, data_dir):
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"""定义开发集的数据是什么，data_dir会作为参数传进去， 这里就是加上你的文件名即可 """
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return self._create_examples("amki_train")
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def get_dev_examples(self, data_dir):
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"""定义开发集的数据是什么，data_dir会作为参数传进去，模型训练的时候会用到，这里就是加上你的文件名即可 """
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return self._create_examples("amki_dev")
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def get_test_examples(self, data_dir):
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"""定义测试集的数据是什么， 用于预测数据 ，在训练时没有用到这个函数， 这里写预测的数据集"""
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return self._create_examples("amki_test")
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def get_labels(self):
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""" 这里是显示你一共有几个分类标签， 在此任务中我有3个标签，如实写上 标签值和 csv里面存的值相同 """
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return [0, 1, 2]
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def _create_examples(self, data_table):
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"""这个函数是用来把数据处理， 把每一个例子分成3个部分，填入到InputExample的3个参数
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text_a 是 第一个句子的文本
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text_b 是 第二个句子的文本 但是由于此任务是单句分类， 所以 这里传入为None
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guid 是一个二元组 第一个表示此数据是什么数据集类型（train dev test） 第二个表示数据标号
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label 表示句子类别
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"""
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examples = []
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for column in dealing_dataset.create_dataset_ep(data_table):
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# 加入样本
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examples.append(
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InputExample(guid=column[0], text_a=column[2], text_b=None, label=column[1]))
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return examples
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class EPBPTProcessor(DataProcessor):
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"""Processor for the Emotion data set ."""
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def get_train_examples(self, data_dir):
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"""定义开发集的数据是什么，data_dir会作为参数传进去， 这里就是加上你的文件名即可 """
|
||
return self._create_examples("amki_train")
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def get_dev_examples(self, data_dir):
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||
"""定义开发集的数据是什么，data_dir会作为参数传进去，模型训练的时候会用到，这里就是加上你的文件名即可 """
|
||
return self._create_examples("amki_dev")
|
||
|
||
def get_test_examples(self, data_dir):
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||
"""定义测试集的数据是什么， 用于预测数据 ，在训练时没有用到这个函数， 这里写预测的数据集"""
|
||
return self._create_examples("amki_test")
|
||
|
||
def get_labels(self):
|
||
""" 这里是显示你一共有几个分类标签， 在此任务中我有3个标签，如实写上 标签值和 csv里面存的值相同 """
|
||
return [0, 1, 2]
|
||
|
||
def _create_examples(self, data_table):
|
||
"""这个函数是用来把数据处理， 把每一个例子分成3个部分，填入到InputExample的3个参数
|
||
text_a 是 第一个句子的文本
|
||
text_b 是 第二个句子的文本 但是由于此任务是单句分类， 所以 这里传入为None
|
||
guid 是一个二元组 第一个表示此数据是什么数据集类型（train dev test） 第二个表示数据标号
|
||
label 表示句子类别
|
||
"""
|
||
examples = []
|
||
for column in dealing_dataset.create_dataset_pdt():
|
||
# 加入样本
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||
examples.append(
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InputExample(guid=column[0], text_a=column[2], text_b=None, label=column[1]))
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return examples
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||
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def convert_single_example(ex_index, example, label_list, max_seq_length,
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tokenizer):
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"""Converts a single `InputExample` into a single `InputFeatures`."""
|
||
|
||
if isinstance(example, PaddingInputExample):
|
||
return InputFeatures(
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input_ids=[0] * max_seq_length,
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input_mask=[0] * max_seq_length,
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||
segment_ids=[0] * max_seq_length,
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||
label_id=0,
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is_real_example=False)
|
||
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||
label_map = {}
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||
for (i, label) in enumerate(label_list):
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||
label_map[label] = i
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||
|
||
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, max_seq_length - 3)
|
||
else:
|
||
# Account for [CLS] and [SEP] with "- 2"
|
||
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,
|
||
is_real_example=True)
|
||
return feature
|
||
|
||
|
||
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."""
|
||
|
||
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)
|
||
|
||
def create_int_feature(values):
|
||
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
|
||
return f
|
||
|
||
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])
|
||
features["is_real_example"] = create_int_feature(
|
||
[int(feature.is_real_example)])
|
||
|
||
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),
|
||
"is_real_example": 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 _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 create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
|
||
labels, num_labels, use_one_hot_embeddings):
|
||
"""Creates a classification model."""
|
||
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)
|
||
|
||
# In the demo, we are doing a simple classification task on the entire
|
||
# segment.
|
||
#
|
||
# If you want to use the token-level output, use model.get_sequence_output()
|
||
# instead.
|
||
output_layer = model.get_pooled_output()
|
||
|
||
hidden_size = output_layer.shape[-1].value
|
||
|
||
output_weights = tf.get_variable(
|
||
"output_weights", [num_labels, hidden_size],
|
||
initializer=tf.truncated_normal_initializer(stddev=0.02))
|
||
|
||
output_bias = tf.get_variable(
|
||
"output_bias", [num_labels], initializer=tf.zeros_initializer())
|
||
|
||
with tf.variable_scope("loss"):
|
||
if is_training:
|
||
# I.e., 0.1 dropout
|
||
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
|
||
|
||
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
|
||
logits = tf.nn.bias_add(logits, output_bias)
|
||
probabilities = tf.nn.softmax(logits, axis=-1)
|
||
log_probs = tf.nn.log_softmax(logits, axis=-1)
|
||
|
||
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
|
||
|
||
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
|
||
loss = tf.reduce_mean(per_example_loss)
|
||
|
||
return (loss, per_example_loss, logits, probabilities)
|
||
|
||
|
||
def model_fn_builder(bert_config, num_labels, 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"]
|
||
label_ids = features["label_ids"]
|
||
is_real_example = None
|
||
if "is_real_example" in features:
|
||
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
|
||
else:
|
||
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
|
||
|
||
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
|
||
|
||
(total_loss, per_example_loss, logits, probabilities) = create_model(
|
||
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
|
||
num_labels, use_one_hot_embeddings)
|
||
|
||
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(per_example_loss, label_ids, logits, is_real_example):
|
||
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
|
||
accuracy = tf.metrics.accuracy(
|
||
labels=label_ids, predictions=predictions, weights=is_real_example)
|
||
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
|
||
return {
|
||
"eval_accuracy": accuracy,
|
||
"eval_loss": loss,
|
||
}
|
||
|
||
eval_metrics = (metric_fn,
|
||
[per_example_loss, label_ids, logits, is_real_example])
|
||
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
|
||
mode=mode,
|
||
loss=total_loss,
|
||
eval_metrics=eval_metrics,
|
||
scaffold_fn=scaffold_fn)
|
||
else:
|
||
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
|
||
mode=mode,
|
||
predictions={"probabilities": probabilities},
|
||
scaffold_fn=scaffold_fn)
|
||
return output_spec
|
||
|
||
return model_fn
|
||
|
||
|
||
# This function is not used by this file but is still used by the Colab and
|
||
# people who depend on it.
|
||
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):
|
||
"""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({
|
||
"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
|
||
|
||
|
||
# This function is not used by this file but is still used by the Colab and
|
||
# people who depend on it.
|
||
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 main(_):
|
||
tf.logging.set_verbosity(tf.logging.INFO)
|
||
|
||
processors = {
|
||
"cola": ColaProcessor,
|
||
"mnli": MnliProcessor,
|
||
"mrpc": MrpcProcessor,
|
||
"xnli": XnliProcessor,
|
||
"ep": EPProcessor,
|
||
"eppdt": EPBPTProcessor,
|
||
}
|
||
|
||
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
|
||
FLAGS.init_checkpoint)
|
||
|
||
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
|
||
raise ValueError(
|
||
"At least one of `do_train`, `do_eval` or `do_predict' must be True.")
|
||
|
||
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)
|
||
|
||
task_name = FLAGS.task_name.lower()
|
||
|
||
if task_name not in processors:
|
||
raise ValueError("Task not found: %s" % (task_name))
|
||
|
||
processor = processors[task_name]()
|
||
|
||
label_list = processor.get_labels()
|
||
|
||
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=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))
|
||
|
||
train_examples = None
|
||
num_train_steps = None
|
||
num_warmup_steps = None
|
||
if FLAGS.do_train:
|
||
train_examples = processor.get_train_examples(FLAGS.data_dir)
|
||
num_train_steps = int(
|
||
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
|
||
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
|
||
|
||
model_fn = model_fn_builder(
|
||
bert_config=bert_config,
|
||
num_labels=len(label_list),
|
||
init_checkpoint=FLAGS.init_checkpoint,
|
||
learning_rate=FLAGS.learning_rate,
|
||
num_train_steps=num_train_steps,
|
||
num_warmup_steps=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,
|
||
predict_batch_size=FLAGS.predict_batch_size)
|
||
|
||
if FLAGS.do_train:
|
||
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
|
||
file_based_convert_examples_to_features(
|
||
train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
|
||
tf.logging.info("***** Running training *****")
|
||
tf.logging.info(" Num examples = %d", len(train_examples))
|
||
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
|
||
tf.logging.info(" Num steps = %d", num_train_steps)
|
||
train_input_fn = file_based_input_fn_builder(
|
||
input_file=train_file,
|
||
seq_length=FLAGS.max_seq_length,
|
||
is_training=True,
|
||
drop_remainder=True)
|
||
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
|
||
|
||
if FLAGS.do_eval:
|
||
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
|
||
num_actual_eval_examples = len(eval_examples)
|
||
if FLAGS.use_tpu:
|
||
# TPU requires a fixed batch size for all batches, therefore the number
|
||
# of examples must be a multiple of the batch size, or else examples
|
||
# will get dropped. So we pad with fake examples which are ignored
|
||
# later on. These do NOT count towards the metric (all tf.metrics
|
||
# support a per-instance weight, and these get a weight of 0.0).
|
||
while len(eval_examples) % FLAGS.eval_batch_size != 0:
|
||
eval_examples.append(PaddingInputExample())
|
||
|
||
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
|
||
file_based_convert_examples_to_features(
|
||
eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
|
||
|
||
tf.logging.info("***** Running evaluation *****")
|
||
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
|
||
len(eval_examples), num_actual_eval_examples,
|
||
len(eval_examples) - num_actual_eval_examples)
|
||
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
|
||
|
||
# This tells the estimator to run through the entire set.
|
||
eval_steps = None
|
||
# However, if running eval on the TPU, you will need to specify the
|
||
# number of steps.
|
||
if FLAGS.use_tpu:
|
||
assert len(eval_examples) % FLAGS.eval_batch_size == 0
|
||
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
|
||
|
||
eval_drop_remainder = True if FLAGS.use_tpu else False
|
||
eval_input_fn = file_based_input_fn_builder(
|
||
input_file=eval_file,
|
||
seq_length=FLAGS.max_seq_length,
|
||
is_training=False,
|
||
drop_remainder=eval_drop_remainder)
|
||
|
||
result = estimator.evaluate(input_fn=eval_input_fn, steps=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 FLAGS.do_predict:
|
||
predict_examples = processor.get_test_examples(FLAGS.data_dir)
|
||
num_actual_predict_examples = len(predict_examples)
|
||
if FLAGS.use_tpu:
|
||
# TPU requires a fixed batch size for all batches, therefore the number
|
||
# of examples must be a multiple of the batch size, or else examples
|
||
# will get dropped. So we pad with fake examples which are ignored
|
||
# later on.
|
||
while len(predict_examples) % FLAGS.predict_batch_size != 0:
|
||
predict_examples.append(PaddingInputExample())
|
||
|
||
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
|
||
file_based_convert_examples_to_features(predict_examples, label_list,
|
||
FLAGS.max_seq_length, tokenizer,
|
||
predict_file)
|
||
|
||
tf.logging.info("***** Running prediction*****")
|
||
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
|
||
len(predict_examples), num_actual_predict_examples,
|
||
len(predict_examples) - num_actual_predict_examples)
|
||
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
|
||
|
||
predict_drop_remainder = True if FLAGS.use_tpu else False
|
||
predict_input_fn = file_based_input_fn_builder(
|
||
input_file=predict_file,
|
||
seq_length=FLAGS.max_seq_length,
|
||
is_training=False,
|
||
drop_remainder=predict_drop_remainder)
|
||
|
||
result = estimator.predict(input_fn=predict_input_fn)
|
||
|
||
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
|
||
with tf.gfile.GFile(output_predict_file, "w") as writer:
|
||
num_written_lines = 0
|
||
tf.logging.info("***** Predict results *****")
|
||
for (i, prediction) in enumerate(result):
|
||
probabilities = prediction["probabilities"]
|
||
if i >= num_actual_predict_examples:
|
||
break
|
||
output_line = "\t".join(
|
||
str(class_probability)
|
||
for class_probability in probabilities) + "\n"
|
||
writer.write(output_line)
|
||
num_written_lines += 1
|
||
assert num_written_lines == num_actual_predict_examples
|
||
|
||
|
||
if __name__ == "__main__":
|
||
flags.mark_flag_as_required("data_dir")
|
||
flags.mark_flag_as_required("task_name")
|
||
flags.mark_flag_as_required("vocab_file")
|
||
flags.mark_flag_as_required("bert_config_file")
|
||
flags.mark_flag_as_required("output_dir")
|
||
tf.app.run()
|