run_extraction.py

# coding=utf-8
# Copyright 2018 The Google AI 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.
# Lint as: python2, python3

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

import json
import os
import random
import time

from albert import fine_tuning_utils
from albert import modeling
from albert import squad_utils
import six
import tensorflow.compat.v1 as tf

from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
from tensorflow.contrib import tpu as contrib_tpu

# pylint: disable=g-import-not-at-top
if six.PY2:
    import six.moves.cPickle as pickle
else:
    import pickle
# pylint: enable=g-import-not-at-top

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string(
    "albert_config_file", None,
    "The config json file corresponding to the pre-trained ALBERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("vocab_file", None,
                    "The vocabulary file that the ALBERT model was trained on.")

flags.DEFINE_string("spm_model_file", None,
                    "The model file for sentence piece tokenization.")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters
flags.DEFINE_string("train_file", None,
                    "SQuAD json for training. E.g., train-v1.1.json")

flags.DEFINE_string(
    "predict_file", None,
    "SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")

flags.DEFINE_string("train_feature_file", None,
                    "training feature file.")

flags.DEFINE_string(
    "predict_feature_file", None,
    "Location of predict features. If it doesn't exist, it will be written. "
    "If it does exist, it will be read.")

flags.DEFINE_string(
    "predict_example_file", None,
    "Location of predict examples. If it doesn't exist, it will be written. "
    "If it does exist, it will be read.")

flags.DEFINE_string(
    "predict_feature_left_file", None,
    "Location of predict features not passed to TPU. If it doesn't exist, it "
    "will be written. If it does exist, it will be read.")

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_string(
    "albert_hub_module_handle", None,
    "If set, the ALBERT hub module to use.")

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(
    "max_seq_length", 384,
    "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_integer(
    "doc_stride", 128,
    "When splitting up a long document into chunks, how much stride to "
    "take between chunks.")

flags.DEFINE_integer(
    "max_query_length", 64,
    "The maximum number of tokens for the question. Questions longer than "
    "this will be truncated to this length.")

flags.DEFINE_bool("do_train", False, "Whether to run training.")

flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")

flags.DEFINE_integer("predict_batch_size", 8,
                     "Total batch size for predictions.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 100,
                     "How many steps to make in each estimator call.")

flags.DEFINE_integer(
    "n_best_size", 20,
    "The total number of n-best predictions to generate in the "
    "nbest_predictions.json output file.")

flags.DEFINE_integer(
    "max_answer_length", 30,
    "The maximum length of an answer that can be generated. This is needed "
    "because the start and end predictions are not conditioned on one another.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")

flags.DEFINE_integer("start_n_top", 5, "beam size for the start positions.")

flags.DEFINE_integer("end_n_top", 5, "beam size for the end positions.")

flags.DEFINE_float("dropout_prob", 0.1, "dropout probability.")

flags.DEFINE_bool("do_part2", False, "Event extraction part2 or not")

flags.DEFINE_string("tag_info_file", None, "tag info file.")


def validate_flags_or_throw(albert_config):
    """Validate the input FLAGS or throw an exception."""

    if not FLAGS.do_train and not FLAGS.do_predict:
        raise ValueError("At least one of `do_train` or `do_predict` must be True.")

    # if FLAGS.do_train:
    #     if not FLAGS.train_file:
    #         raise ValueError(
    #             "If `do_train` is True, then `train_file` must be specified.")
    if FLAGS.do_predict:
        # if not FLAGS.predict_file:
        #     raise ValueError(
        #         "If `do_predict` is True, then `predict_file` must be specified.")
        if not FLAGS.predict_feature_file:
            raise ValueError(
                "If `do_predict` is True, then `predict_feature_file` must be "
                "specified.")
        if not FLAGS.predict_feature_left_file:
            raise ValueError(
                "If `do_predict` is True, then `predict_feature_left_file` must be "
                "specified.")

    if FLAGS.max_seq_length > albert_config.max_position_embeddings:
        raise ValueError(
            "Cannot use sequence length %d because the ALBERT model "
            "was only trained up to sequence length %d" %
            (FLAGS.max_seq_length, albert_config.max_position_embeddings))

    if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:
        raise ValueError(
            "The max_seq_length (%d) must be greater than max_query_length "
            "(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length))


def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)

    albert_config = modeling.AlbertConfig.from_json_file(FLAGS.albert_config_file)

    validate_flags_or_throw(albert_config)

    tf.gfile.MakeDirs(FLAGS.output_dir)
    #
    # tokenizer = fine_tuning_utils.create_vocab(
    #     vocab_file=FLAGS.vocab_file,
    #     do_lower_case=FLAGS.do_lower_case,
    #     spm_model_file=FLAGS.spm_model_file,
    #     hub_module=FLAGS.albert_hub_module_handle)

    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

    is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
    if FLAGS.do_train:
        iterations_per_loop = int(min(FLAGS.iterations_per_loop,
                                      FLAGS.save_checkpoints_steps))
    else:
        iterations_per_loop = FLAGS.iterations_per_loop
    run_config = contrib_tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        keep_checkpoint_max=0,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=contrib_tpu.TPUConfig(
            iterations_per_loop=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 not tf.gfile.Exists(FLAGS.train_feature_file):
        raise Exception("Train tf-record missed...")
    cnt = 0
    records = tf.python_io.tf_record_iterator(FLAGS.train_feature_file)
    for _ in records:
        cnt += 1
    print(cnt)
    num_train_steps = int(
        cnt / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    # train_examples = squad_utils.read_squad_examples(
    #     input_file=FLAGS.train_file, is_training=True)
    # num_train_steps = int(
    #     len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    if FLAGS.do_train:
        num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
    #
    #   # Pre-shuffle the input to avoid having to make a very large shuffle
    #   # buffer in in the `input_fn`.
    #   rng = random.Random(12345)
    #   rng.shuffle(train_examples)

    tag_info = squad_utils.TagInfo.load(FLAGS.tag_info_file)
    print(tag_info.__dict__)

    model_fn = squad_utils.v2_model_fn_builder(
        albert_config=albert_config,
        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,
        max_seq_length=FLAGS.max_seq_length,
        start_n_top=FLAGS.start_n_top,
        end_n_top=FLAGS.end_n_top,
        dropout_prob=FLAGS.dropout_prob,
        hub_module=FLAGS.albert_hub_module_handle,
        tag_info=tag_info)

    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = contrib_tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)

    if FLAGS.do_train:
        # We write to a temporary file to avoid storing very large constant tensors
        # in memory.

        # if not tf.gfile.Exists(FLAGS.train_feature_file):
        #     train_writer = squad_utils.FeatureWriter(
        #         filename=os.path.join(FLAGS.train_feature_file), is_training=True)
        #     squad_utils.convert_examples_to_features(
        #         examples=train_examples,
        #         tokenizer=tokenizer,
        #         max_seq_length=FLAGS.max_seq_length,
        #         doc_stride=FLAGS.doc_stride,
        #         max_query_length=FLAGS.max_query_length,
        #         is_training=True,
        #         output_fn=train_writer.process_feature,
        #         do_lower_case=FLAGS.do_lower_case)
        #     train_writer.close()

        tf.logging.info("***** Running training *****")
        # tf.logging.info("  Num orig examples = %d", len(train_examples))
        # tf.logging.info("  Num split examples = %d", train_writer.num_features)
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        # del train_examples

        train_input_fn = squad_utils.input_fn_builder(
            input_file=FLAGS.train_feature_file,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True,
            use_tpu=FLAGS.use_tpu,
            bsz=FLAGS.train_batch_size,
            is_v2=True)
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

    if FLAGS.do_predict:
        import dill
        # with tf.gfile.Open(FLAGS.predict_file) as predict_file:
        #     prediction_json = json.load(predict_file)["data"]
        # eval_examples = squad_utils.read_squad_examples(
        #     input_file=FLAGS.predict_file, is_training=False)

        if (tf.gfile.Exists(FLAGS.predict_feature_file) and tf.gfile.Exists(
                FLAGS.predict_feature_left_file) and tf.gfile.Exists(FLAGS.predict_example_file)):
            tf.logging.info("Loading eval features from {}".format(
                FLAGS.predict_feature_left_file))
            with tf.gfile.Open(FLAGS.predict_feature_left_file, "rb") as fin:
                eval_features = dill.load(fin)
            with tf.gfile.Open(FLAGS.predict_example_file, "rb") as fin:
                eval_examples = dill.load(fin)
        # else:
        #     eval_writer = squad_utils.FeatureWriter(
            #     filename=FLAGS.predict_feature_file, is_training=False)
            # eval_features = []
            #
            # def append_feature(feature):
            #     eval_features.append(feature)
            #     eval_writer.process_feature(feature)
            #
            # squad_utils.convert_examples_to_features(
            #     examples=eval_examples,
            #     tokenizer=tokenizer,
            #     max_seq_length=FLAGS.max_seq_length,
            #     doc_stride=FLAGS.doc_stride,
            #     max_query_length=FLAGS.max_query_length,
            #     is_training=False,
            #     output_fn=append_feature,
            #     do_lower_case=FLAGS.do_lower_case)
            # eval_writer.close()
            #
            # with tf.gfile.Open(FLAGS.predict_feature_left_file, "wb") as fout:
            #     pickle.dump(eval_features, fout)

        tf.logging.info("***** Running predictions *****")
        tf.logging.info("  Num orig examples = %d", len(eval_examples))
        tf.logging.info("  Num split examples = %d", len(eval_features))
        tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)

        predict_input_fn = squad_utils.input_fn_builder(
            input_file=FLAGS.predict_feature_file,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=False,
            use_tpu=FLAGS.use_tpu,
            bsz=FLAGS.predict_batch_size,
            is_v2=True)

        def get_result(checkpoint):
            """Evaluate the checkpoint on SQuAD v2.0."""
            # If running eval on the TPU, you will need to specify the number of
            # steps.
            all_results = []
            for result in estimator.predict(
                    predict_input_fn, yield_single_examples=True,
                    checkpoint_path=checkpoint):
                if len(all_results) % 1000 == 0:
                    tf.logging.info("Processing example: %d" % (len(all_results)))
                unique_id = int(result["unique_ids"])
                crf_logits = result["crf_logits"]
                transition_params = result["transition_params"]
                softmax = result["softmax"]
                all_results.append(
                    squad_utils.RawResultV2(
                        unique_id=unique_id,
                        crf_logits=crf_logits,
                        softmax=softmax,
                        transition_params=transition_params,
                    ))

            output_prediction_file = os.path.join(
                FLAGS.output_dir, "predictions.json")

            predictions = squad_utils.write_predictions_et(eval_examples, eval_features, all_results,
                                                           FLAGS.max_answer_length, tag_info)

            import numpy
            class MyEncoder(json.JSONEncoder):
                def default(self, o):
                    if isinstance(o, numpy.integer):
                        return int(o)
                    if isinstance(o, numpy.floating):
                        return float(o)
                    if isinstance(o, numpy.ndarray):
                        return o.tolist()
                    else:
                        return super(MyEncoder, self).default(o)

            with tf.gfile.Open(output_prediction_file, 'w') as f:
                json.dump(predictions, f, ensure_ascii=False, cls=MyEncoder)

        latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
        get_result(latest_checkpoint)


if __name__ == "__main__":
    # flags.mark_flag_as_required("spm_model_file")
    flags.mark_flag_as_required("albert_config_file")
    flags.mark_flag_as_required("output_dir")
    tf.app.run()