# 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. """ALBERT finetuning runner with sentence piece tokenization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time from albert import classifier_utils from albert import fine_tuning_utils from albert import modeling from albert import race_utils import tensorflow.compat.v1 as tf from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver from tensorflow.contrib import tpu as contrib_tpu flags = tf.flags FLAGS = flags.FLAGS ## Required parameters flags.DEFINE_string( "data_dir", None, "The input data dir. Should contain the .tsv files (or other data files) " "for the task.") 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("task_name", "race", "The name of the task to train.") flags.DEFINE_string("vocab_file", None, "The vocabulary file that the ALBERT model was trained on.") flags.DEFINE_string("train_file", None, "path to preprocessed tfrecord file. " "The file will be generated if not exst.") flags.DEFINE_string("eval_file", None, "path to preprocessed tfrecord file. " "The file will be generated if not exst.") flags.DEFINE_string("predict_file", None, "path to preprocessed tfrecord file. " "The file will be generated if not exst.") 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( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained ALBERT 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_float("dropout_prob", 0.1, "dropout probability.") flags.DEFINE_integer( "max_seq_length", 512, "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( "max_qa_length", 128, "The maximum total input sequence length after WordPiece tokenization. " "Sequences longer than this will be truncated, and sequences shorter " "than this will be padded.") flags.DEFINE_integer( "num_keep_checkpoint", 5, "maximum number of keep checkpoints") flags.DEFINE_bool( "high_only", False, "Whether to only run the model on the high school set.") flags.DEFINE_bool( "middle_only", False, "Whether to only run the model on the middle school set.") flags.DEFINE_bool("do_train", True, "Whether to run training.") flags.DEFINE_bool("do_eval", True, "Whether to run eval on the dev set.") flags.DEFINE_bool( "do_predict", False, "Whether to run the model in inference mode on the test set.") flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.") flags.DEFINE_float("learning_rate", 1e-5, "The initial learning rate for Adam.") flags.DEFINE_integer("train_step", 12000, "Total number of training epochs to perform.") flags.DEFINE_integer( "warmup_step", 1000, "number of steps to perform linear learning rate warmup for.") flags.DEFINE_integer("save_checkpoints_steps", 100, "How often to save the model checkpoint.") flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") 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.") def main(_): tf.logging.set_verbosity(tf.logging.INFO) processors = { "race": race_utils.RaceProcessor } 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.") albert_config = modeling.AlbertConfig.from_json_file(FLAGS.albert_config_file) 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)) 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]( use_spm=True if FLAGS.spm_model_file else False, do_lower_case=FLAGS.do_lower_case, high_only=FLAGS.high_only, middle_only=FLAGS.middle_only) label_list = processor.get_labels() 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, save_checkpoints_steps=int(FLAGS.save_checkpoints_steps), keep_checkpoint_max=0, 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 if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) model_fn = race_utils.model_fn_builder( albert_config=albert_config, num_labels=len(label_list), init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=FLAGS.train_step, num_warmup_steps=FLAGS.warmup_step, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu, max_seq_length=FLAGS.max_seq_length, dropout_prob=FLAGS.dropout_prob, hub_module=FLAGS.albert_hub_module_handle) # 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, eval_batch_size=FLAGS.eval_batch_size, predict_batch_size=FLAGS.predict_batch_size) if FLAGS.do_train: if not tf.gfile.Exists(FLAGS.train_file): race_utils.file_based_convert_examples_to_features( train_examples, label_list, FLAGS.max_seq_length, tokenizer, FLAGS.train_file, FLAGS.max_qa_length) 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", FLAGS.train_step) train_input_fn = classifier_utils.file_based_input_fn_builder( input_file=FLAGS.train_file, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True, task_name=task_name, use_tpu=FLAGS.use_tpu, bsz=FLAGS.train_batch_size, multiple=len(label_list)) estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_step) 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(classifier_utils.PaddingInputExample()) if not tf.gfile.Exists(FLAGS.eval_file): race_utils.file_based_convert_examples_to_features( eval_examples, label_list, FLAGS.max_seq_length, tokenizer, FLAGS.eval_file, FLAGS.max_qa_length) 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 = classifier_utils.file_based_input_fn_builder( input_file=FLAGS.eval_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder, task_name=task_name, use_tpu=FLAGS.use_tpu, bsz=FLAGS.eval_batch_size, multiple=len(label_list)) def _find_valid_cands(curr_step): filenames = tf.gfile.ListDirectory(FLAGS.output_dir) candidates = [] for filename in filenames: if filename.endswith(".index"): ckpt_name = filename[:-6] idx = ckpt_name.split("-")[-1] if idx != "best" and int(idx) > curr_step: candidates.append(filename) return candidates output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best") key_name = "eval_accuracy" if tf.gfile.Exists(checkpoint_path + ".index"): result = estimator.evaluate( input_fn=eval_input_fn, steps=eval_steps, checkpoint_path=checkpoint_path) best_perf = result[key_name] global_step = result["global_step"] else: global_step = -1 best_perf = -1 checkpoint_path = None writer = tf.gfile.GFile(output_eval_file, "w") while global_step < FLAGS.train_step: steps_and_files = {} filenames = tf.gfile.ListDirectory(FLAGS.output_dir) for filename in filenames: if filename.endswith(".index"): ckpt_name = filename[:-6] cur_filename = os.path.join(FLAGS.output_dir, ckpt_name) if cur_filename.split("-")[-1] == "best": continue gstep = int(cur_filename.split("-")[-1]) if gstep not in steps_and_files: tf.logging.info("Add {} to eval list.".format(cur_filename)) steps_and_files[gstep] = cur_filename tf.logging.info("found {} files.".format(len(steps_and_files))) # steps_and_files = sorted(steps_and_files, key=lambda x: x[0]) if not steps_and_files: tf.logging.info("found 0 file, global step: {}. Sleeping." .format(global_step)) time.sleep(1) else: for ele in sorted(steps_and_files.items()): step, checkpoint_path = ele if global_step >= step: if len(_find_valid_cands(step)) > 1: for ext in ["meta", "data-00000-of-00001", "index"]: src_ckpt = checkpoint_path + ".{}".format(ext) tf.logging.info("removing {}".format(src_ckpt)) tf.gfile.Remove(src_ckpt) continue result = estimator.evaluate( input_fn=eval_input_fn, steps=eval_steps, checkpoint_path=checkpoint_path) global_step = result["global_step"] 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]))) writer.write("best = {}\n".format(best_perf)) if result[key_name] > best_perf: best_perf = result[key_name] for ext in ["meta", "data-00000-of-00001", "index"]: src_ckpt = checkpoint_path + ".{}".format(ext) tgt_ckpt = checkpoint_path.rsplit("-", 1)[0] + "-best.{}".format(ext) tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt)) tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True) writer.write("saved {} to {}\n".format(src_ckpt, tgt_ckpt)) if len(_find_valid_cands(global_step)) > 1: for ext in ["meta", "data-00000-of-00001", "index"]: src_ckpt = checkpoint_path + ".{}".format(ext) tf.logging.info("removing {}".format(src_ckpt)) tf.gfile.Remove(src_ckpt) writer.write("=" * 50 + "\n") writer.close() 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(classifier_utils.PaddingInputExample()) assert len(predict_examples) % FLAGS.predict_batch_size == 0 predict_steps = int(len(predict_examples) // FLAGS.predict_batch_size) predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") race_utils.file_based_convert_examples_to_features( predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file, FLAGS.max_qa_length) 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 = classifier_utils.file_based_input_fn_builder( input_file=predict_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder, task_name=task_name, use_tpu=FLAGS.use_tpu, bsz=FLAGS.predict_batch_size, multiple=len(label_list)) checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best") result = estimator.evaluate( input_fn=predict_input_fn, steps=predict_steps, checkpoint_path=checkpoint_path) output_predict_file = os.path.join(FLAGS.output_dir, "predict_results.txt") with tf.gfile.GFile(output_predict_file, "w") as pred_writer: # num_written_lines = 0 tf.logging.info("***** Predict results *****") pred_writer.write("***** Predict results *****\n") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) pred_writer.write("%s = %s\n" % (key, str(result[key]))) pred_writer.write("best = {}\n".format(best_perf)) if __name__ == "__main__": flags.mark_flag_as_required("data_dir") 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()