# 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. """BERT finetuning runner.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import csv import os import modeling import optimization import tokenization import tensorflow as tf 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( "bert_config_file", None, "The config json file corresponding to the pre-trained BERT model. " "This specifies the model architecture.") flags.DEFINE_string("task_name", None, "The name of the task to train.") flags.DEFINE_string("vocab_file", None, "The vocabulary file that the BERT model was trained on.") 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_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", 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_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_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", 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.") class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_id): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" with tf.gfile.Open(input_file, "r") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: lines.append(line) return lines class MnliProcessor(DataProcessor): """Processor for the MultiNLI data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched") def get_labels(self): """See base class.""" return ["contradiction", "entailment", "neutral"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0])) text_a = tokenization.convert_to_unicode(line[8]) text_b = tokenization.convert_to_unicode(line[9]) label = tokenization.convert_to_unicode(line[-1]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class MrpcProcessor(DataProcessor): """Processor for the MRPC data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" print("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv"))) return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = tokenization.convert_to_unicode(line[3]) text_b = tokenization.convert_to_unicode(line[4]) label = tokenization.convert_to_unicode(line[0]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class ColaProcessor(DataProcessor): """Processor for the CoLA data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) text_a = tokenization.convert_to_unicode(line[3]) label = tokenization.convert_to_unicode(line[1]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer): """Loads a data file into a list of `InputBatch`s.""" label_map = {} for (i, label) in enumerate(label_list): label_map[label] = i features = [] for (ex_index, example) in enumerate(examples): tokens_a = tokenizer.tokenize(example.text_a) tokens_b = None if example.text_b: tokens_b = tokenizer.tokenize(example.text_b) if tokens_b: # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) else: # Account for [CLS] and [SEP] with "- 2" if len(tokens_a) > max_seq_length - 2: tokens_a = tokens_a[0:(max_seq_length - 2)] # The convention in BERT is: # (a) For sequence pairs: # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 # (b) For single sequences: # tokens: [CLS] the dog is hairy . [SEP] # type_ids: 0 0 0 0 0 0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambigiously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. 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) if tokens_b: for token in tokens_b: tokens.append(token) segment_ids.append(1) tokens.append("[SEP]") segment_ids.append(1) input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) # Zero-pad up to the sequence 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 label_id = label_map[example.label] if ex_index < 5: tf.logging.info("*** Example ***") tf.logging.info("guid: %s" % (example.guid)) tf.logging.info("tokens: %s" % " ".join( [tokenization.printable_text(x) for x in tokens])) tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) tf.logging.info( "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) tf.logging.info("label: %s (id = %d)" % (example.label, label_id)) features.append( InputFeatures( input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id)) return features def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels, use_one_hot_embeddings): """Creates a classification model.""" 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) # In the demo, we are doing a simple classification task on the entire # segment. # # If you want to use the token-level output, use model.get_sequence_output() # instead. output_layer = model.get_pooled_output() hidden_size = output_layer.shape[-1].value output_weights = tf.get_variable( "output_weights", [num_labels, hidden_size], initializer=tf.truncated_normal_initializer(stddev=0.02)) output_bias = tf.get_variable( "output_bias", [num_labels], initializer=tf.zeros_initializer()) with tf.variable_scope("loss"): if is_training: # I.e., 0.1 dropout output_layer = tf.nn.dropout(output_layer, keep_prob=0.9) logits = tf.matmul(output_layer, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) one_hot_labels = tf.one_hot(labels, depth=num_labels, 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, logits) def model_fn_builder(bert_config, num_labels, 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"] label_ids = features["label_ids"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) (total_loss, per_example_loss, logits) = create_model( bert_config, is_training, input_ids, input_mask, segment_ids, label_ids, num_labels, use_one_hot_embeddings) tvars = tf.trainable_variables() 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(per_example_loss, label_ids, logits): predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) accuracy = tf.metrics.accuracy(label_ids, predictions) loss = tf.metrics.mean(per_example_loss) return { "eval_accuracy": accuracy, "eval_loss": loss, } eval_metrics = (metric_fn, [per_example_loss, label_ids, logits]) 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 input_fn_builder(features, seq_length, is_training, drop_remainder): """Creates an `input_fn` closure to be passed to TPUEstimator.""" all_input_ids = [] all_input_mask = [] all_segment_ids = [] all_label_ids = [] for feature in features: all_input_ids.append(feature.input_ids) all_input_mask.append(feature.input_mask) all_segment_ids.append(feature.segment_ids) all_label_ids.append(feature.label_id) def input_fn(params): """The actual input function.""" batch_size = params["batch_size"] num_examples = len(features) # This is for demo purposes and does NOT scale to large data sets. We do # not use Dataset.from_generator() because that uses tf.py_func which is # not TPU compatible. The right way to load data is with TFRecordReader. d = tf.data.Dataset.from_tensor_slices({ "input_ids": tf.constant( all_input_ids, shape=[num_examples, seq_length], dtype=tf.int32), "input_mask": tf.constant( all_input_mask, shape=[num_examples, seq_length], dtype=tf.int32), "segment_ids": tf.constant( all_segment_ids, shape=[num_examples, seq_length], dtype=tf.int32), "label_ids": tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32), }) if is_training: d = d.repeat() d = d.shuffle(buffer_size=100) d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder) return d return input_fn def main(_): tf.logging.set_verbosity(tf.logging.INFO) processors = { "cola": ColaProcessor, "mnli": MnliProcessor, "mrpc": MrpcProcessor, } 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) if FLAGS.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length %d because the BERT model " "was only trained up to sequence length %d" % (FLAGS.max_seq_length, bert_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]() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer( vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) 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)) train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) model_fn = model_fn_builder( bert_config=bert_config, num_labels=len(label_list), 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) # 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: train_features = convert_examples_to_features( train_examples, label_list, FLAGS.max_seq_length, tokenizer) 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", num_train_steps) train_input_fn = input_fn_builder( features=train_features, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True) estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) if FLAGS.do_eval: eval_examples = processor.get_dev_examples(FLAGS.data_dir) eval_features = convert_examples_to_features( eval_examples, label_list, FLAGS.max_seq_length, tokenizer) tf.logging.info("***** Running evaluation *****") tf.logging.info(" Num examples = %d", len(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: # Eval will be slightly WRONG on the TPU because it will truncate # the last batch. eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size) eval_drop_remainder = True if FLAGS.use_tpu else False eval_input_fn = input_fn_builder( features=eval_features, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=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("data_dir") flags.mark_flag_as_required("task_name") flags.mark_flag_as_required("vocab_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") tf.app.run()