# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Run masked LM/next sentence masked_lm pre-training for BERT.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import modeling import optimization import tensorflow as tf import horovod.tensorflow as hvd from tensorflow.python import debug as tf_debug flags = tf.flags FLAGS = flags.FLAGS ## Required parameters flags.DEFINE_string( "bert_config_file", None, "The config json file corresponding to the pre-trained BERT model. " "This specifies the model architecture.") flags.DEFINE_string( "input_file", None, "Input TF example files (can be a glob or comma separated).") flags.DEFINE_string( "validation_input_file", None, "Input validation TF example files (can be a glob or comma separated).") flags.DEFINE_string( "input_dir", None, "Input TF example dir.") flags.DEFINE_string( "validation_input_dir", None, "Input validation TF example dir.") 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 BERT model).") flags.DEFINE_integer( "max_seq_length", 128, "The maximum total input sequence length after WordPiece tokenization. " "Sequences longer than this will be truncated, and sequences shorter " "than this will be padded. Must match data generation.") flags.DEFINE_integer( "max_predictions_per_seq", 20, "Maximum number of masked LM predictions per sequence. " "Must match data generation.") flags.DEFINE_bool("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_bool("do_train_eval", False, "Whether to run train with eval.") 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_float("learning_rate", 5e-5, "The initial learning rate for Adam.") flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.") flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.") flags.DEFINE_integer("save_checkpoints_steps", 1000, "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_integer("max_eval_steps", None, "Maximum number of eval steps.") 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("hooking_frequence", 100, "Hooking frequence.") flags.DEFINE_bool("reduce_log", False, "Reduce log.") flags.DEFINE_integer("keep_checkpoint_max", 1, "Keep checkpoint max.") flags.DEFINE_bool("xla", True, "Whether to train with XLA optimization.") flags.DEFINE_bool("adjust_lr", True, "Whether to adjust learning_rate.") flags.DEFINE_integer("previous_train_steps", 0, "Previous train steps.") flags.DEFINE_integer("post_train_steps", 0, "Post train steps.") flags.DEFINE_bool("use_hvd", True, "Whether to use Horovod.") flags.DEFINE_bool("use_compression", True, "Whether to use compression in Horovod.") flags.DEFINE_bool("use_fp16", True, "Whether to use fp16.") flags.DEFINE_bool("cos_decay", False, "Whether to use cos decay.") flags.DEFINE_bool("use_lamb", False, "Whether to use lamb.") flags.DEFINE_bool("auto_recover", False, "Whether to use auto recover.") flags.DEFINE_string("recover_dir", None, "The output directory where the model checkpoints will be recovered.") flags.DEFINE_integer("ckpt_no", None, "Checkpoint number of model to be recovered.") flags.DEFINE_integer("ckpt_no_input", None, "Checkpoint number of input to be recovered.") flags.DEFINE_bool("clip", False, "Whether to use clip.") flags.DEFINE_bool("profile", False, "Whether to use profile.") def model_fn_builder(bert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings, adjust_lr, use_hvd, use_compression, use_fp16, clip, cos_decay, use_lamb, previous_train_steps, post_train_steps): """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"] masked_lm_positions = features["masked_lm_positions"] masked_lm_ids = features["masked_lm_ids"] masked_lm_weights = features["masked_lm_weights"] next_sentence_labels = features["next_sentence_labels"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) 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, compute_type=tf.float16 if use_fp16 else tf.float32) (masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output( bert_config, model.get_sequence_output(), model.get_embedding_table(), masked_lm_positions, masked_lm_ids, masked_lm_weights, clip) (next_sentence_loss, next_sentence_example_loss, next_sentence_log_probs) = get_next_sentence_output( bert_config, model.get_pooled_output(), next_sentence_labels, clip) total_loss = masked_lm_loss + next_sentence_loss 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, update_learning_rate = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, adjust_lr, use_hvd, use_compression, use_fp16, clip, cos_decay, use_lamb, previous_train_steps, post_train_steps) logging_hook = tf.train.LoggingTensorHook({"loss": total_loss, "learning_rate": update_learning_rate}, every_n_iter=FLAGS.hooking_frequence) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, training_hooks=[logging_hook]) elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels): """Computes the loss and accuracy of the model.""" masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]]) masked_lm_predictions = tf.argmax( masked_lm_log_probs, axis=-1, output_type=tf.int32) masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1]) masked_lm_ids = tf.reshape(masked_lm_ids, [-1]) masked_lm_weights = tf.reshape(masked_lm_weights, [-1]) masked_lm_accuracy = tf.metrics.accuracy( labels=masked_lm_ids, predictions=masked_lm_predictions, weights=masked_lm_weights) masked_lm_mean_loss = tf.metrics.mean( values=masked_lm_example_loss, weights=masked_lm_weights) next_sentence_log_probs = tf.reshape( next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]]) next_sentence_predictions = tf.argmax( next_sentence_log_probs, axis=-1, output_type=tf.int32) next_sentence_labels = tf.reshape(next_sentence_labels, [-1]) next_sentence_accuracy = tf.metrics.accuracy( labels=next_sentence_labels, predictions=next_sentence_predictions) next_sentence_mean_loss = tf.metrics.mean( values=next_sentence_example_loss) return { "masked_lm_accuracy": masked_lm_accuracy, "masked_lm_loss": masked_lm_mean_loss, "next_sentence_accuracy": next_sentence_accuracy, "next_sentence_loss": next_sentence_mean_loss, } eval_metrics = metric_fn( masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels ) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, eval_metric_ops=eval_metrics) else: raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode)) return output_spec return model_fn def get_masked_lm_output(bert_config, input_tensor, output_weights, positions, label_ids, label_weights, clip): """Get loss and log probs for the masked LM.""" input_tensor = gather_indexes(input_tensor, positions) with tf.variable_scope("cls/predictions"): # We apply one more non-linear transformation before the output layer. # This matrix is not used after pre-training. with tf.variable_scope("transform"): input_tensor = tf.layers.dense( input_tensor, units=bert_config.hidden_size, activation=modeling.get_activation(bert_config.hidden_act), kernel_initializer=modeling.create_initializer( bert_config.initializer_range)) input_tensor = modeling.layer_norm(input_tensor) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. output_bias = tf.get_variable( "output_bias", shape=[bert_config.vocab_size], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) if clip: log_probs = tf.log(tf.clip_by_value(tf.nn.softmax(logits, axis=-1), 1e-6, 1.0 - 1e-6)) else: log_probs = tf.nn.log_softmax(logits, axis=-1) label_ids = tf.reshape(label_ids, [-1]) label_weights = tf.reshape(label_weights, [-1]) one_hot_labels = tf.one_hot( label_ids, depth=bert_config.vocab_size, dtype=tf.float32) # The `positions` tensor might be zero-padded (if the sequence is too # short to have the maximum number of predictions). The `label_weights` # tensor has a value of 1.0 for every real prediction and 0.0 for the # padding predictions. per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1]) numerator = tf.reduce_sum(label_weights * per_example_loss) denominator = tf.reduce_sum(label_weights) + 1e-5 loss = numerator / denominator return (loss, per_example_loss, log_probs) def get_next_sentence_output(bert_config, input_tensor, labels, clip): """Get loss and log probs for the next sentence prediction.""" # Simple binary classification. Note that 0 is "next sentence" and 1 is # "random sentence". This weight matrix is not used after pre-training. with tf.variable_scope("cls/seq_relationship"): output_weights = tf.get_variable( "output_weights", shape=[2, bert_config.hidden_size], initializer=modeling.create_initializer(bert_config.initializer_range)) output_bias = tf.get_variable( "output_bias", shape=[2], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) if clip: log_probs = tf.log(tf.clip_by_value(tf.nn.softmax(logits, axis=-1), 1e-6, 1.0 - 1e-6)) else: log_probs = tf.nn.log_softmax(logits, axis=-1) labels = tf.reshape(labels, [-1]) one_hot_labels = tf.one_hot(labels, depth=2, 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, log_probs) def gather_indexes(sequence_tensor, positions): """Gathers the vectors at the specific positions over a minibatch.""" sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3) batch_size = sequence_shape[0] seq_length = sequence_shape[1] width = sequence_shape[2] flat_offsets = tf.reshape( tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]) flat_positions = tf.reshape(positions + flat_offsets, [-1]) flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length, width]) output_tensor = tf.gather(flat_sequence_tensor, flat_positions) return output_tensor def input_fn_builder(input_files, max_seq_length, max_predictions_per_seq, is_training, num_cpu_threads=4, batch_size=None, use_hvd=True): """Creates an `input_fn` closure to be passed to TPUEstimator.""" def input_fn(params): """The actual input function.""" # batch_size = params["batch_size"] name_to_features = { "input_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "input_mask": tf.FixedLenFeature([max_seq_length], tf.int64), "segment_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "masked_lm_positions": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_ids": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_weights": tf.FixedLenFeature([max_predictions_per_seq], tf.float32), "next_sentence_labels": tf.FixedLenFeature([1], tf.int64), } # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. if is_training: d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) if use_hvd: d = d.shard(hvd.size(), hvd.rank()) #TODO only for Horovod, shard to mimic single_GPU = False print("Data shard: %s %s" % (hvd.size(), hvd.rank())) d = d.repeat() d = d.shuffle(buffer_size=len(input_files)) # `cycle_length` is the number of parallel files that get read. cycle_length = min(num_cpu_threads, len(input_files)) # `sloppy` mode means that the interleaving is not exact. This adds # even more randomness to the training pipeline. d = d.apply( tf.contrib.data.parallel_interleave( tf.data.TFRecordDataset, sloppy=is_training, cycle_length=cycle_length)) d = d.shuffle(buffer_size=100) else: d = tf.data.TFRecordDataset(input_files) # Since we evaluate for a fixed number of steps we don't want to encounter # out-of-range exceptions. # d = d.repeat() # We must `drop_remainder` on training because the TPU requires fixed # size dimensions. For eval, we assume we are evaluating on the CPU or GPU # and we *don't* want to drop the remainder, otherwise we wont cover # every sample. d = d.apply( tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, num_parallel_batches=num_cpu_threads, drop_remainder=True)) return d return input_fn 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 main(_): tf.logging.set_verbosity(tf.logging.INFO) if FLAGS.use_hvd: hvd.init() if FLAGS.reduce_log and (hvd.rank() != 0): tf.logging.set_verbosity(tf.logging.ERROR) FLAGS.output_dir = FLAGS.output_dir if hvd.rank() == 0 else os.path.join(FLAGS.output_dir, str(hvd.rank())) if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_train_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) tf.gfile.MakeDirs(FLAGS.output_dir) if FLAGS.recover_dir is not None: if FLAGS.use_hvd: FLAGS.recover_dir = FLAGS.recover_dir if hvd.rank() == 0 else os.path.join(FLAGS.recover_dir, str(hvd.rank())) path_ckpt = os.path.join(FLAGS.output_dir, "checkpoint") path_ckpt_input = os.path.join(FLAGS.output_dir, "checkpoint_input") if FLAGS.ckpt_no is not None and not tf.gfile.Exists(path_ckpt): with tf.gfile.GFile(path_ckpt, "w") as writer: writer.write('model_checkpoint_path: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "model.ckpt"), str(FLAGS.ckpt_no))) writer.write('all_model_checkpoint_paths: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "model.ckpt"), str(FLAGS.ckpt_no))) if FLAGS.ckpt_no_input is not None and not tf.gfile.Exists(path_ckpt_input): with tf.gfile.GFile(path_ckpt_input, "w") as writer: writer.write('model_checkpoint_path: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "input.ckpt"), str(FLAGS.ckpt_no_input))) writer.write('all_model_checkpoint_paths: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "input.ckpt"), str(FLAGS.ckpt_no_input))) if FLAGS.use_hvd and hvd.rank() == 0 and (FLAGS.do_train or FLAGS.do_train_eval): (cpath, cname) = os.path.split(FLAGS.bert_config_file) tf.gfile.Copy(FLAGS.bert_config_file, os.path.join(FLAGS.output_dir, cname), True) input_files = [] if FLAGS.input_file is not None: for input_pattern in FLAGS.input_file.split(","): input_files.extend(tf.gfile.Glob(input_pattern)) if FLAGS.input_dir is not None: for filename in tf.gfile.ListDirectory(FLAGS.input_dir): input_files.extend(tf.gfile.Glob(os.path.join(FLAGS.input_dir, filename))) tf.logging.info("*** Input Files ***") for input_file in input_files: tf.logging.info(" %s" % input_file) validation_input_files = [] if FLAGS.validation_input_file is None and FLAGS.validation_input_dir is None: validation_input_files = input_files else: if FLAGS.validation_input_file is not None: for input_pattern in FLAGS.validation_input_file.split(","): validation_input_files.extend(tf.gfile.Glob(input_pattern)) if FLAGS.validation_input_dir is not None: for filename in tf.gfile.ListDirectory(FLAGS.validation_input_dir): validation_input_files.extend(tf.gfile.Glob(os.path.join(FLAGS.validation_input_dir, filename))) tf.logging.info("*** Input Validation Files ***") for input_file in validation_input_files: tf.logging.info(" %s" % input_file) config = tf.ConfigProto() if FLAGS.xla: config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 if FLAGS.use_hvd: config.gpu_options.visible_device_list = str(hvd.local_rank()) config.gpu_options.allow_growth=True run_config = tf.estimator.RunConfig( model_dir=FLAGS.output_dir, keep_checkpoint_max=FLAGS.keep_checkpoint_max, save_checkpoints_steps=FLAGS.save_checkpoints_steps, log_step_count_steps=FLAGS.hooking_frequence, session_config=config) if FLAGS.use_hvd and hvd.rank() != 0 and not FLAGS.auto_recover: run_config = tf.estimator.RunConfig( model_dir=FLAGS.output_dir, keep_checkpoint_max=FLAGS.keep_checkpoint_max, save_checkpoints_steps=None, save_checkpoints_secs=None, log_step_count_steps=FLAGS.hooking_frequence, session_config=config) model_fn = model_fn_builder( bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=FLAGS.num_train_steps, num_warmup_steps=FLAGS.num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu, adjust_lr=FLAGS.adjust_lr, use_hvd=FLAGS.use_hvd, use_compression=FLAGS.use_compression, use_fp16=FLAGS.use_fp16, clip=FLAGS.clip, cos_decay=FLAGS.cos_decay, use_lamb=FLAGS.use_lamb, previous_train_steps=FLAGS.previous_train_steps, post_train_steps=FLAGS.post_train_steps) hooks = [] if FLAGS.use_hvd: hooks.append(hvd.BroadcastGlobalVariablesHook(0)) if hvd.rank() == -1: #if debug, set 0 CLIDebugHook = tf_debug.LocalCLIDebugHook(ui_type='readline') CLIDebugHook.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan) hooks.append(CLIDebugHook) if FLAGS.profile and hvd.rank() == 0: ProfilerHook = tf.train.ProfilerHook(save_steps=FLAGS.hooking_frequence, output_dir=FLAGS.output_dir, show_dataflow=True, show_memory=True) hooks.append(ProfilerHook) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.estimator.Estimator( model_fn=model_fn, config=run_config) if FLAGS.do_train: tf.logging.info("***** Running training *****") tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) train_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True, batch_size=FLAGS.train_batch_size, use_hvd=FLAGS.use_hvd) if FLAGS.auto_recover: hooks.append(tf.data.experimental.CheckpointInputPipelineHook(estimator)) estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps, hooks=hooks) if FLAGS.do_eval: tf.logging.info("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_input_fn = input_fn_builder( input_files=validation_input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=False, batch_size=FLAGS.eval_batch_size, use_hvd=FLAGS.use_hvd) result = estimator.evaluate( input_fn=eval_input_fn, steps=FLAGS.max_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_train_eval: tf.logging.info("***** Running training *****") tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) train_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True, batch_size=FLAGS.train_batch_size, use_hvd=FLAGS.use_hvd) tf.logging.info("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_input_fn = input_fn_builder( input_files=validation_input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=False, batch_size=FLAGS.eval_batch_size, use_hvd=FLAGS.use_hvd) if FLAGS.auto_recover: hooks.append(tf.data.experimental.CheckpointInputPipelineHook(estimator)) train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps, hooks=hooks) eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, steps=FLAGS.max_eval_steps) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) if __name__ == "__main__": # flags.mark_flag_as_required("input_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") tf.app.run()