Delete sentence_prediction_dataloader.py

sentence_prediction_dataloader.py

@@ -1,267 +0,0 @@
-# Copyright 2024 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."""
-import dataclasses
-import functools
-from typing import List, Mapping, Optional, Tuple
-
-import tensorflow as tf, tf_keras
-import tensorflow_hub as hub
-
-from official.common import dataset_fn
-from official.core import config_definitions as cfg
-from official.core import input_reader
-from official.nlp import modeling
-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
-  label_field: str = 'label_ids'
-  # Maps the key in TfExample to feature name.
-  # E.g 'label_ids' to 'next_sentence_labels'
-  label_name: Optional[Tuple[str, str]] = None
-  # Either tfrecord, sstable, or recordio.
-  file_type: str = 'tfrecord'
-
-
-@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
-    self._label_field = params.label_field
-    if params.label_name:
-      self._label_name_mapping = dict([params.label_name])
-    else:
-      self._label_name_mapping = dict()
-
-  def name_to_features_spec(self):
-    """Defines features to decode. Subclass may override to append features."""
-    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),
-        self._label_field: tf.io.FixedLenFeature([], label_type),
-    }
-    if self._include_example_id:
-      name_to_features['example_id'] = tf.io.FixedLenFeature([], tf.int64)
-
-    return name_to_features
-
-  def _decode(self, record: tf.Tensor):
-    """Decodes a serialized tf.Example."""
-    example = tf.io.parse_single_example(record, self.name_to_features_spec())
-
-    # 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."""
-    key_mapping = {
-        'input_ids': 'input_word_ids',
-        'input_mask': 'input_mask',
-        'segment_ids': 'input_type_ids'
-    }
-    ret = {}
-    for record_key in record:
-      if record_key in key_mapping:
-        ret[key_mapping[record_key]] = record[record_key]
-      else:
-        ret[record_key] = record[record_key]
-
-    if self._label_field in self._label_name_mapping:
-      ret[self._label_name_mapping[self._label_field]] = record[
-          self._label_field]
-
-    return ret
-
-  def load(self, input_context: Optional[tf.distribute.InputContext] = None):
-    """Returns a tf.dataset.Dataset."""
-    reader = input_reader.InputReader(
-        dataset_fn=dataset_fn.pick_dataset_fn(self._params.file_type),
-        params=self._params,
-        decoder_fn=self._decode,
-        parser_fn=self._parse)
-    return reader.read(input_context)
-
-
-@dataclasses.dataclass
-class SentencePredictionTextDataConfig(cfg.DataConfig):
-  """Data config for sentence prediction task with raw text."""
-  # Either set `input_path`...
-  input_path: str = ''
-  # Either `int` or `float`.
-  label_type: str = 'int'
-  # ...or `tfds_name` and `tfds_split` to specify input.
-  tfds_name: str = ''
-  tfds_split: str = ''
-  # The name of the text feature fields. The text features will be
-  # concatenated in order.
-  text_fields: Optional[List[str]] = None
-  label_field: str = 'label'
-  global_batch_size: int = 32
-  seq_length: int = 128
-  is_training: bool = True
-  # Either build preprocessing with Python code by specifying these values
-  # for modeling.layers.BertTokenizer()/SentencepieceTokenizer()....
-  tokenization: str = 'WordPiece'  # WordPiece or SentencePiece
-  # Text vocab file if tokenization is WordPiece, or sentencepiece.ModelProto
-  # file if tokenization is SentencePiece.
-  vocab_file: str = ''
-  lower_case: bool = True
-  # ...or load preprocessing from a SavedModel at this location.
-  preprocessing_hub_module_url: str = ''
-  # Either tfrecord or sstsable or recordio.
-  file_type: str = 'tfrecord'
-  include_example_id: bool = False
-
-
-class TextProcessor(tf.Module):
-  """Text features processing for sentence prediction task."""
-
-  def __init__(self,
-               seq_length: int,
-               vocab_file: Optional[str] = None,
-               tokenization: Optional[str] = None,
-               lower_case: Optional[bool] = True,
-               preprocessing_hub_module_url: Optional[str] = None):
-    if preprocessing_hub_module_url:
-      self._preprocessing_hub_module = hub.load(preprocessing_hub_module_url)
-      self._tokenizer = self._preprocessing_hub_module.tokenize
-      self._pack_inputs = functools.partial(
-          self._preprocessing_hub_module.bert_pack_inputs,
-          seq_length=seq_length)
-      return
-
-    if tokenization == 'WordPiece':
-      self._tokenizer = modeling.layers.BertTokenizer(
-          vocab_file=vocab_file, lower_case=lower_case)
-    elif tokenization == 'SentencePiece':
-      self._tokenizer = modeling.layers.SentencepieceTokenizer(
-          model_file_path=vocab_file,
-          lower_case=lower_case,
-          strip_diacritics=True)  # Strip diacritics to follow ALBERT model
-    else:
-      raise ValueError('Unsupported tokenization: %s' % tokenization)
-
-    self._pack_inputs = modeling.layers.BertPackInputs(
-        seq_length=seq_length,
-        special_tokens_dict=self._tokenizer.get_special_tokens_dict())
-
-  def __call__(self, segments):
-    segments = [self._tokenizer(s) for s in segments]
-    # BertTokenizer returns a RaggedTensor with shape [batch, word, subword],
-    # and SentencepieceTokenizer returns a RaggedTensor with shape
-    # [batch, sentencepiece],
-    segments = [
-        tf.cast(x.merge_dims(1, -1) if x.shape.rank > 2 else x, tf.int32)
-        for x in segments
-    ]
-    return self._pack_inputs(segments)
-
-
-@data_loader_factory.register_data_loader_cls(SentencePredictionTextDataConfig)
-class SentencePredictionTextDataLoader(data_loader.DataLoader):
-  """Loads dataset with raw text for sentence prediction task."""
-
-  def __init__(self, params):
-    if bool(params.tfds_name) != bool(params.tfds_split):
-      raise ValueError('`tfds_name` and `tfds_split` should be specified or '
-                       'unspecified at the same time.')
-    if bool(params.tfds_name) == bool(params.input_path):
-      raise ValueError('Must specify either `tfds_name` and `tfds_split` '
-                       'or `input_path`.')
-    if not params.text_fields:
-      raise ValueError('Unexpected empty text fields.')
-    if bool(params.vocab_file) == bool(params.preprocessing_hub_module_url):
-      raise ValueError('Must specify exactly one of vocab_file (with matching '
-                       'lower_case flag) or preprocessing_hub_module_url.')
-
-    self._params = params
-    self._text_fields = params.text_fields
-    self._label_field = params.label_field
-    self._label_type = params.label_type
-    self._include_example_id = params.include_example_id
-    self._text_processor = TextProcessor(
-        seq_length=params.seq_length,
-        vocab_file=params.vocab_file,
-        tokenization=params.tokenization,
-        lower_case=params.lower_case,
-        preprocessing_hub_module_url=params.preprocessing_hub_module_url)
-
-  def _bert_preprocess(self, record: Mapping[str, tf.Tensor]):
-    """Berts preprocess."""
-    segments = [record[x] for x in self._text_fields]
-    model_inputs = self._text_processor(segments)
-    for key in record:
-      if key not in self._text_fields:
-        model_inputs[key] = record[key]
-    return model_inputs
-
-  def name_to_features_spec(self):
-    name_to_features = {}
-    for text_field in self._text_fields:
-      name_to_features[text_field] = tf.io.FixedLenFeature([], tf.string)
-
-    label_type = LABEL_TYPES_MAP[self._label_type]
-    name_to_features[self._label_field] = tf.io.FixedLenFeature([], label_type)
-    if self._include_example_id:
-      name_to_features['example_id'] = tf.io.FixedLenFeature([], tf.int64)
-    return name_to_features
-
-  def _decode(self, record: tf.Tensor):
-    """Decodes a serialized tf.Example."""
-    example = tf.io.parse_single_example(record, self.name_to_features_spec())
-    # 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 load(self, input_context: Optional[tf.distribute.InputContext] = None):
-    """Returns a tf.dataset.Dataset."""
-    reader = input_reader.InputReader(
-        dataset_fn=dataset_fn.pick_dataset_fn(self._params.file_type),
-        decoder_fn=self._decode if self._params.input_path else None,
-        params=self._params,
-        postprocess_fn=self._bert_preprocess)
-    return reader.read(input_context)