sentence_prediction_dataloader.py

# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Loads dataset for the sentence prediction (classification) task."""
from typing import Mapping, Optional

import dataclasses
import tensorflow as tf

from official.core import input_reader
from official.modeling.hyperparams import config_definitions as cfg
from official.nlp.data import data_loader
from official.nlp.data import data_loader_factory

LABEL_TYPES_MAP = {'int': tf.int64, 'float': tf.float32}


@dataclasses.dataclass
class SentencePredictionDataConfig(cfg.DataConfig):
  """Data config for sentence prediction task (tasks/sentence_prediction)."""
  input_path: str = ''
  global_batch_size: int = 32
  is_training: bool = True
  seq_length: int = 128
  label_type: str = 'int'
  # Whether to include the example id number.
  include_example_id: bool = False


@data_loader_factory.register_data_loader_cls(SentencePredictionDataConfig)
class SentencePredictionDataLoader(data_loader.DataLoader):
  """A class to load dataset for sentence prediction (classification) task."""

  def __init__(self, params):
    self._params = params
    self._seq_length = params.seq_length
    self._include_example_id = params.include_example_id

  def _decode(self, record: tf.Tensor):
    """Decodes a serialized tf.Example."""
    label_type = LABEL_TYPES_MAP[self._params.label_type]
    name_to_features = {
        'input_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
        'input_mask': tf.io.FixedLenFeature([self._seq_length], tf.int64),
        'segment_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
        'label_ids': tf.io.FixedLenFeature([], label_type),
    }
    if self._include_example_id:
      name_to_features['example_id'] = tf.io.FixedLenFeature([], tf.int64)

    example = tf.io.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 example:
      t = example[name]
      if t.dtype == tf.int64:
        t = tf.cast(t, tf.int32)
      example[name] = t

    return example

  def _parse(self, record: Mapping[str, tf.Tensor]):
    """Parses raw tensors into a dict of tensors to be consumed by the model."""
    x = {
        'input_word_ids': record['input_ids'],
        'input_mask': record['input_mask'],
        'input_type_ids': record['segment_ids']
    }
    if self._include_example_id:
      x['example_id'] = record['example_id']

    y = record['label_ids']
    return (x, y)

  def load(self, input_context: Optional[tf.distribute.InputContext] = None):
    """Returns a tf.dataset.Dataset."""
    reader = input_reader.InputReader(
        params=self._params, decoder_fn=self._decode, parser_fn=self._parse)
    return reader.read(input_context)