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| # 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 | |
| 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( | |
| "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_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", 100, "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.") | |
| def model_fn_builder(bert_config, 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"] | |
| 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) | |
| (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) | |
| (next_sentence_loss, next_sentence_example_loss, | |
| next_sentence_log_probs) = get_next_sentence_output( | |
| bert_config, model.get_pooled_output(), next_sentence_labels) | |
| 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 = 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(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.contrib.tpu.TPUEstimatorSpec( | |
| mode=mode, | |
| loss=total_loss, | |
| eval_metrics=eval_metrics, | |
| scaffold_fn=scaffold_fn) | |
| 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): | |
| """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) | |
| 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): | |
| """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) | |
| 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): | |
| """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)) | |
| 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 not FLAGS.do_train and not FLAGS.do_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) | |
| input_files = [] | |
| for input_pattern in FLAGS.input_file.split(","): | |
| input_files.extend(tf.gfile.Glob(input_pattern)) | |
| tf.logging.info("*** Input Files ***") | |
| for input_file in input_files: | |
| tf.logging.info(" %s" % input_file) | |
| 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)) | |
| 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) | |
| # 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) | |
| 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) | |
| estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps) | |
| 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=input_files, | |
| max_seq_length=FLAGS.max_seq_length, | |
| max_predictions_per_seq=FLAGS.max_predictions_per_seq, | |
| is_training=False) | |
| 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 __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() | |