From 5676d6f799afd75f17a4b14c6ca2ee11f1a5ea08 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Sat, 3 Nov 2018 08:17:22 -0400 Subject: [PATCH] Remove BERT pretraining files for now --- create_pretraining_data_pytorch.py | 429 --------------------------- run_pretraining_pytorch.py | 460 ----------------------------- 2 files changed, 889 deletions(-) delete mode 100644 create_pretraining_data_pytorch.py delete mode 100644 run_pretraining_pytorch.py diff --git a/create_pretraining_data_pytorch.py b/create_pretraining_data_pytorch.py deleted file mode 100644 index 1068bddaff..0000000000 --- a/create_pretraining_data_pytorch.py +++ /dev/null @@ -1,429 +0,0 @@ -# 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. -"""Create masked LM/next sentence masked_lm TF examples for BERT.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import random - -import tokenization -import tensorflow as tf - -import argparse - -parser = argparse.ArgumentParser() - -## Required parameters -parser.add_argument("--input_file", default=None, type=str, required=True, - help="Input raw text file (or comma-separated list of files).") -parser.add_argument("--output_file", default=None, type=str, required=True, - help="Output TF example file (or comma-separated list of files).") -parser.add_argument("--vocab_file", default=None, type=str, required=True, - help="The vocabulary file that the BERT model was trained on.") - -## Other parameters -parser.add_argument("--do_lower_case", default=True, action='store_true', - help="Whether to lower case the input text. Should be True for uncased " - "models and False for cased models.") -parser.add_argument("--max_seq_length", default=128, type=int, help="Maximum sequence length.") -parser.add_argument("--max_predictions_per_seq", default=20, type=int, - help="Maximum number of masked LM predictions per sequence.") -parser.add_argument("--random_seed", default=12345, type=int, help="Random seed for data generation.") -parser.add_argument("--dupe_factor", default=10, type=int, - help="Number of times to duplicate the input data (with different masks).") -parser.add_argument("--masked_lm_prob", default=0.15, type=float, help="Masked LM probability.") -parser.add_argument("--short_seq_prob", default=0.1, type=float, - help="Probability of creating sequences which are shorter than the maximum length.") - -args = parser.parse_args() - - -class TrainingInstance(object): - """A single training instance (sentence pair).""" - - def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels, - is_random_next): - self.tokens = tokens - self.segment_ids = segment_ids - self.is_random_next = is_random_next - self.masked_lm_positions = masked_lm_positions - self.masked_lm_labels = masked_lm_labels - - def __str__(self): - s = "" - s += "tokens: %s\n" % (" ".join( - [tokenization.printable_text(x) for x in self.tokens])) - s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids])) - s += "is_random_next: %s\n" % self.is_random_next - s += "masked_lm_positions: %s\n" % (" ".join( - [str(x) for x in self.masked_lm_positions])) - s += "masked_lm_labels: %s\n" % (" ".join( - [tokenization.printable_text(x) for x in self.masked_lm_labels])) - s += "\n" - return s - - def __repr__(self): - return self.__str__() - - -def write_instance_to_example_files(instances, tokenizer, max_seq_length, - max_predictions_per_seq, output_files): - """Create TF example files from `TrainingInstance`s.""" - writers = [] - for output_file in output_files: - writers.append(tf.python_io.TFRecordWriter(output_file)) - - writer_index = 0 - - total_written = 0 - for (inst_index, instance) in enumerate(instances): - input_ids = tokenizer.convert_tokens_to_ids(instance.tokens) - input_mask = [1] * len(input_ids) - segment_ids = list(instance.segment_ids) - assert len(input_ids) <= max_seq_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 - - masked_lm_positions = list(instance.masked_lm_positions) - masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels) - masked_lm_weights = [1.0] * len(masked_lm_ids) - - while len(masked_lm_positions) < max_predictions_per_seq: - masked_lm_positions.append(0) - masked_lm_ids.append(0) - masked_lm_weights.append(0.0) - - next_sentence_label = 1 if instance.is_random_next else 0 - - features = collections.OrderedDict() - features["input_ids"] = create_int_feature(input_ids) - features["input_mask"] = create_int_feature(input_mask) - features["segment_ids"] = create_int_feature(segment_ids) - features["masked_lm_positions"] = create_int_feature(masked_lm_positions) - features["masked_lm_ids"] = create_int_feature(masked_lm_ids) - features["masked_lm_weights"] = create_float_feature(masked_lm_weights) - features["next_sentence_labels"] = create_int_feature([next_sentence_label]) - - tf_example = tf.train.Example(features=tf.train.Features(feature=features)) - - writers[writer_index].write(tf_example.SerializeToString()) - writer_index = (writer_index + 1) % len(writers) - - total_written += 1 - - if inst_index < 20: - tf.logging.info("*** Example ***") - tf.logging.info("tokens: %s" % " ".join( - [tokenization.printable_text(x) for x in instance.tokens])) - - for feature_name in features.keys(): - feature = features[feature_name] - values = [] - if feature.int64_list.value: - values = feature.int64_list.value - elif feature.float_list.value: - values = feature.float_list.value - tf.logging.info( - "%s: %s" % (feature_name, " ".join([str(x) for x in values]))) - - for writer in writers: - writer.close() - - tf.logging.info("Wrote %d total instances", total_written) - - -def create_int_feature(values): - feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) - return feature - - -def create_float_feature(values): - feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) - return feature - - -def create_training_instances(input_files, tokenizer, max_seq_length, - dupe_factor, short_seq_prob, masked_lm_prob, - max_predictions_per_seq, rng): - """Create `TrainingInstance`s from raw text.""" - all_documents = [[]] - - # Input file format: - # (1) One sentence per line. These should ideally be actual sentences, not - # entire paragraphs or arbitrary spans of text. (Because we use the - # sentence boundaries for the "next sentence prediction" task). - # (2) Blank lines between documents. Document boundaries are needed so - # that the "next sentence prediction" task doesn't span between documents. - for input_file in input_files: - with tf.gfile.GFile(input_file, "r") as reader: - while True: - line = tokenization.convert_to_unicode(reader.readline()) - if not line: - break - line = line.strip() - - # Empty lines are used as document delimiters - if not line: - all_documents.append([]) - tokens = tokenizer.tokenize(line) - if tokens: - all_documents[-1].append(tokens) - - # Remove empty documents - all_documents = [x for x in all_documents if x] - rng.shuffle(all_documents) - - vocab_words = list(tokenizer.vocab.keys()) - instances = [] - for _ in range(dupe_factor): - for document_index in range(len(all_documents)): - instances.extend( - create_instances_from_document( - all_documents, document_index, max_seq_length, short_seq_prob, - masked_lm_prob, max_predictions_per_seq, vocab_words, rng)) - - rng.shuffle(instances) - return instances - - -def create_instances_from_document( - all_documents, document_index, max_seq_length, short_seq_prob, - masked_lm_prob, max_predictions_per_seq, vocab_words, rng): - """Creates `TrainingInstance`s for a single document.""" - document = all_documents[document_index] - - # Account for [CLS], [SEP], [SEP] - max_num_tokens = max_seq_length - 3 - - # We *usually* want to fill up the entire sequence since we are padding - # to `max_seq_length` anyways, so short sequences are generally wasted - # computation. However, we *sometimes* - # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter - # sequences to minimize the mismatch between pre-training and fine-tuning. - # The `target_seq_length` is just a rough target however, whereas - # `max_seq_length` is a hard limit. - target_seq_length = max_num_tokens - if rng.random() < short_seq_prob: - target_seq_length = rng.randint(2, max_num_tokens) - - # We DON'T just concatenate all of the tokens from a document into a long - # sequence and choose an arbitrary split point because this would make the - # next sentence prediction task too easy. Instead, we split the input into - # segments "A" and "B" based on the actual "sentences" provided by the user - # input. - instances = [] - current_chunk = [] - current_length = 0 - i = 0 - while i < len(document): - segment = document[i] - current_chunk.append(segment) - current_length += len(segment) - if i == len(document) - 1 or current_length >= target_seq_length: - if current_chunk: - # `a_end` is how many segments from `current_chunk` go into the `A` - # (first) sentence. - a_end = 1 - if len(current_chunk) >= 2: - a_end = rng.randint(1, len(current_chunk) - 1) - - tokens_a = [] - for j in range(a_end): - tokens_a.extend(current_chunk[j]) - - tokens_b = [] - # Random next - is_random_next = False - if len(current_chunk) == 1 or rng.random() < 0.5: - is_random_next = True - target_b_length = target_seq_length - len(tokens_a) - - # This should rarely go for more than one iteration for large - # corpora. However, just to be careful, we try to make sure that - # the random document is not the same as the document - # we're processing. - for _ in range(10): - random_document_index = rng.randint(0, len(all_documents) - 1) - if random_document_index != document_index: - break - - random_document = all_documents[random_document_index] - random_start = rng.randint(0, len(random_document) - 1) - for j in range(random_start, len(random_document)): - tokens_b.extend(random_document[j]) - if len(tokens_b) >= target_b_length: - break - # We didn't actually use these segments so we "put them back" so - # they don't go to waste. - num_unused_segments = len(current_chunk) - a_end - i -= num_unused_segments - # Actual next - else: - is_random_next = False - for j in range(a_end, len(current_chunk)): - tokens_b.extend(current_chunk[j]) - truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng) - - assert len(tokens_a) >= 1 - assert len(tokens_b) >= 1 - - 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) - - for token in tokens_b: - tokens.append(token) - segment_ids.append(1) - tokens.append("[SEP]") - segment_ids.append(1) - - (tokens, masked_lm_positions, - masked_lm_labels) = create_masked_lm_predictions( - tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng) - instance = TrainingInstance( - tokens=tokens, - segment_ids=segment_ids, - is_random_next=is_random_next, - masked_lm_positions=masked_lm_positions, - masked_lm_labels=masked_lm_labels) - instances.append(instance) - current_chunk = [] - current_length = 0 - i += 1 - - return instances - - -def create_masked_lm_predictions(tokens, masked_lm_prob, - max_predictions_per_seq, vocab_words, rng): - """Creates the predictis for the masked LM objective.""" - - cand_indexes = [] - for (i, token) in enumerate(tokens): - if token == "[CLS]" or token == "[SEP]": - continue - cand_indexes.append(i) - - rng.shuffle(cand_indexes) - - output_tokens = list(tokens) - - masked_lm = collections.namedtuple("masked_lm", ["index", "label"]) # pylint: disable=invalid-name - - num_to_predict = min(max_predictions_per_seq, - max(1, int(round(len(tokens) * masked_lm_prob)))) - - masked_lms = [] - covered_indexes = set() - for index in cand_indexes: - if len(masked_lms) >= num_to_predict: - break - if index in covered_indexes: - continue - covered_indexes.add(index) - - masked_token = None - # 80% of the time, replace with [MASK] - if rng.random() < 0.8: - masked_token = "[MASK]" - else: - # 10% of the time, keep original - if rng.random() < 0.5: - masked_token = tokens[index] - # 10% of the time, replace with random word - else: - masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)] - - output_tokens[index] = masked_token - - masked_lms.append(masked_lm(index=index, label=tokens[index])) - - masked_lms = sorted(masked_lms, key=lambda x: x.index) - - masked_lm_positions = [] - masked_lm_labels = [] - for p in masked_lms: - masked_lm_positions.append(p.index) - masked_lm_labels.append(p.label) - - return (output_tokens, masked_lm_positions, masked_lm_labels) - - -def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng): - """Truncates a pair of sequences to a maximum sequence length.""" - while True: - total_length = len(tokens_a) + len(tokens_b) - if total_length <= max_num_tokens: - break - - trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b - assert len(trunc_tokens) >= 1 - - # We want to sometimes truncate from the front and sometimes from the - # back to add more randomness and avoid biases. - if rng.random() < 0.5: - del trunc_tokens[0] - else: - trunc_tokens.pop() - - -def main(_): - tf.logging.set_verbosity(tf.logging.INFO) - - tokenizer = tokenization.FullTokenizer( - vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) - - input_files = [] - for input_pattern in args.input_file.split(","): - input_files.extend(tf.gfile.Glob(input_pattern)) - - tf.logging.info("*** Reading from input files ***") - for input_file in input_files: - tf.logging.info(" %s", input_file) - - rng = random.Random(args.random_seed) - instances = create_training_instances( - input_files, tokenizer, args.max_seq_length, args.dupe_factor, - args.short_seq_prob, args.masked_lm_prob, args.max_predictions_per_seq, - rng) - - output_files = args.output_file.split(",") - tf.logging.info("*** Writing to output files ***") - for output_file in output_files: - tf.logging.info(" %s", output_file) - - write_instance_to_example_files(instances, tokenizer, args.max_seq_length, - args.max_predictions_per_seq, output_files) - - -if __name__ == "__main__": - tf.app.run() diff --git a/run_pretraining_pytorch.py b/run_pretraining_pytorch.py deleted file mode 100644 index 6ffd576eaa..0000000000 --- a/run_pretraining_pytorch.py +++ /dev/null @@ -1,460 +0,0 @@ -# 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 argparse - - -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_assigment_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 evaling 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(_): - parser = argparse.ArgumentParser() - - ## Required parameters - parser.add_argument("--bert_config_file", default=None, type=str, required=True, - help="The config json file corresponding to the pre-trained BERT model. " - "This specifies the model architecture.") - parser.add_argument("--input_file", default=None, type=str, required=True, - help="Input TF example files (can be a glob or comma separated).") - parser.add_argument("--output_dir", default=None, type=str, required=True, - help="The output directory where the model checkpoints will be written.") - - ## Other parameters - parser.add_argument("--init_checkpoint", default=None, type=str, - help="Initial checkpoint (usually from a pre-trained BERT model).") - parser.add_argument("--max_seq_length", default=128, type=int, - help="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.") - parser.add_argument("--max_predictions_per_seq", default=20, type=int, - help="Maximum number of masked LM predictions per sequence. Must match data generation.") - parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.") - parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.") - parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") - parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") - parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") - parser.add_argument("--num_train_steps", default=100000, type=int, help="Number of training steps.") - parser.add_argument("--num_warmup_steps", default=10000, type=int, help="Number of warmup steps.") - parser.add_argument("--save_checkpoints_steps", default=1000, type=int, - help="How often to save the model checkpoint.") - parser.add_argument("--iterations_per_loop", default=1000, type=int, - help="How many steps to make in each estimator call.") - parser.add_argument("--max_eval_steps", default=100, type=int, help="Maximum number of eval steps.") - ### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ### - parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.") - parser.add_argument("--tpu_name", default=None, type=str, - help="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.") - parser.add_argument("--tpu_zone", default=None, type=str, - help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt " - "to automatically detect the GCE project from metadata.") - parser.add_argument("--gcp_project", default=None, type=str, - help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, " - "we will attempt to automatically detect the GCE project from metadata.") - parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.") - parser.add_argument("--num_tpu_cores", default=8, type=int, - help="Only used if `use_tpu` is True. Total number of TPU cores to use.") - ### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ### - - args = parser.parse_args() - - tf.logging.set_verbosity(tf.logging.INFO) - - if not args.do_train and not args.do_eval: - raise ValueError("At least one of `do_train` or `do_eval` must be True.") - - bert_config = modeling.BertConfig.from_json_file(args.bert_config_file) - - tf.gfile.MakeDirs(args.output_dir) - - input_files = [] - for input_pattern in args.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 args.use_tpu and args.tpu_name: - tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( - args.tpu_name, zone=args.tpu_zone, project=args.gcp_project) - - is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 - run_config = tf.contrib.tpu.RunConfig( - cluster=tpu_cluster_resolver, - master=args.master, - model_dir=args.output_dir, - save_checkpoints_steps=args.save_checkpoints_steps, - tpu_config=tf.contrib.tpu.TPUConfig( - iterations_per_loop=args.iterations_per_loop, - num_shards=args.num_tpu_cores, - per_host_input_for_training=is_per_host)) - - model_fn = model_fn_builder( - bert_config=bert_config, - init_checkpoint=args.init_checkpoint, - learning_rate=args.learning_rate, - num_train_steps=args.num_train_steps, - num_warmup_steps=args.num_warmup_steps, - use_tpu=args.use_tpu, - use_one_hot_embeddings=args.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=args.use_tpu, - model_fn=model_fn, - config=run_config, - train_batch_size=args.train_batch_size, - eval_batch_size=args.eval_batch_size) - - if args.do_train: - tf.logging.info("***** Running training *****") - tf.logging.info(" Batch size = %d", args.train_batch_size) - train_input_fn = input_fn_builder( - input_files=input_files, - max_seq_length=args.max_seq_length, - max_predictions_per_seq=args.max_predictions_per_seq, - is_training=True) - estimator.train(input_fn=train_input_fn, max_steps=args.num_train_steps) - - if args.do_eval: - tf.logging.info("***** Running evaluation *****") - tf.logging.info(" Batch size = %d", args.eval_batch_size) - - eval_input_fn = input_fn_builder( - input_files=input_files, - max_seq_length=args.max_seq_length, - max_predictions_per_seq=args.max_predictions_per_seq, - is_training=False) - - result = estimator.evaluate( - input_fn=eval_input_fn, steps=args.max_eval_steps) - - output_eval_file = os.path.join(args.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__": - tf.app.run()