Convert flags to argparse in 'run_pretraining_pytorch.py'

run_pretraining_pytorch.py

+# 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
+
+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, type=bool, help="Whether to run training.")
+parser.add_argument("--do_eval", default=False, type=bool, 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, type=bool, 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()
+
+
+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(_):
+    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()