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* Preprocessing script * short seq prob * comments + env vars * Add preprocessing reference. Add test * lint fix + add eval test support * whitespaces * point to commit * comment * rename * better comments
128 lines
3.9 KiB
Python
128 lines
3.9 KiB
Python
# https://github.com/mlcommons/training/blob/1c8a098ae3e70962a4f7422c0b0bd35ae639e357/language_model/tensorflow/bert/cleanup_scripts/pick_eval_samples.py
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# NOTE: This is a direct copy of the original script
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"""Script for picking certain number of sampels.
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"""
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import argparse
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import time
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import logging
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import collections
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import tensorflow as tf
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parser = argparse.ArgumentParser(
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description="Eval sample picker for BERT.")
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parser.add_argument(
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'--input_tfrecord',
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type=str,
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default='',
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help='Input tfrecord path')
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parser.add_argument(
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'--output_tfrecord',
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type=str,
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default='',
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help='Output tfrecord path')
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parser.add_argument(
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'--num_examples_to_pick',
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type=int,
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default=10000,
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help='Number of examples to pick')
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parser.add_argument(
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'--max_seq_length',
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type=int,
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default=512,
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help='The maximum number of tokens within a sequence.')
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parser.add_argument(
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'--max_predictions_per_seq',
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type=int,
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default=76,
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help='The maximum number of predictions within a sequence.')
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args = parser.parse_args()
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max_seq_length = args.max_seq_length
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max_predictions_per_seq = args.max_predictions_per_seq
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logging.basicConfig(level=logging.INFO)
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def decode_record(record):
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"""Decodes a record to a TensorFlow example."""
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name_to_features = {
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"input_ids":
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tf.FixedLenFeature([max_seq_length], tf.int64),
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"input_mask":
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tf.FixedLenFeature([max_seq_length], tf.int64),
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"segment_ids":
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tf.FixedLenFeature([max_seq_length], tf.int64),
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"masked_lm_positions":
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tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
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"masked_lm_ids":
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tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
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"masked_lm_weights":
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tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
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"next_sentence_labels":
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tf.FixedLenFeature([1], tf.int64),
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}
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example = tf.parse_single_example(record, name_to_features)
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# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
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# So cast all int64 to int32.
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for name in list(example.keys()):
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t = example[name]
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if t.dtype == tf.int64:
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t = tf.to_int32(t)
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example[name] = t
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return example
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def create_int_feature(values):
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feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
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return feature
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def create_float_feature(values):
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feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
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return feature
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if __name__ == '__main__':
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tic = time.time()
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tf.enable_eager_execution()
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d = tf.data.TFRecordDataset(args.input_tfrecord)
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num_examples = 0
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records = []
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for record in d:
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records.append(record)
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num_examples += 1
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writer = tf.python_io.TFRecordWriter(args.output_tfrecord)
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i = 0
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pick_ratio = num_examples / args.num_examples_to_pick
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num_examples_picked = 0
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for i in range(args.num_examples_to_pick):
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example = decode_record(records[int(i * pick_ratio)])
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features = collections.OrderedDict()
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features["input_ids"] = create_int_feature(
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example["input_ids"].numpy())
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features["input_mask"] = create_int_feature(
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example["input_mask"].numpy())
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features["segment_ids"] = create_int_feature(
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example["segment_ids"].numpy())
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features["masked_lm_positions"] = create_int_feature(
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example["masked_lm_positions"].numpy())
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features["masked_lm_ids"] = create_int_feature(
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example["masked_lm_ids"].numpy())
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features["masked_lm_weights"] = create_float_feature(
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example["masked_lm_weights"].numpy())
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features["next_sentence_labels"] = create_int_feature(
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example["next_sentence_labels"].numpy())
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tf_example = tf.train.Example(features=tf.train.Features(feature=features))
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writer.write(tf_example.SerializeToString())
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num_examples_picked += 1
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writer.close()
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toc = time.time()
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logging.info("Picked %d examples out of %d samples in %.2f sec",
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num_examples_picked, num_examples, toc - tic)
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