sentence_prediction.py

# Copyright 2021 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.

"""Sentence prediction (classification) task."""
from typing import List, Union, Optional

from absl import logging
import dataclasses
import numpy as np
import orbit
from scipy import stats
from sklearn import metrics as sklearn_metrics
import tensorflow as tf

from official.core import base_task
from official.core import config_definitions as cfg
from official.core import task_factory
from official.modeling import tf_utils
from official.modeling.hyperparams import base_config
from official.nlp.configs import encoders
from official.nlp.data import data_loader_factory
from official.nlp.modeling import models
from official.nlp.tasks import utils

METRIC_TYPES = frozenset(
    ['accuracy', 'matthews_corrcoef', 'pearson_spearman_corr'])


@dataclasses.dataclass
class ModelConfig(base_config.Config):
  """A classifier/regressor configuration."""
  num_classes: int = 0
  use_encoder_pooler: bool = False
  encoder: encoders.EncoderConfig = encoders.EncoderConfig()


@dataclasses.dataclass
class SentencePredictionConfig(cfg.TaskConfig):
  """The model config."""
  # At most one of `init_checkpoint` and `hub_module_url` can
  # be specified.
  init_checkpoint: str = ''
  init_cls_pooler: bool = False
  hub_module_url: str = ''
  metric_type: str = 'accuracy'
  # Defines the concrete model config at instantiation time.
  model: ModelConfig = ModelConfig()
  train_data: cfg.DataConfig = cfg.DataConfig()
  validation_data: cfg.DataConfig = cfg.DataConfig()


@task_factory.register_task_cls(SentencePredictionConfig)
class SentencePredictionTask(base_task.Task):
  """Task object for sentence_prediction."""

  def __init__(self, params: cfg.TaskConfig, logging_dir=None, name=None):
    super().__init__(params, logging_dir, name=name)
    if params.metric_type not in METRIC_TYPES:
      raise ValueError('Invalid metric_type: {}'.format(params.metric_type))
    self.metric_type = params.metric_type
    if hasattr(params.train_data, 'label_field'):
      self.label_field = params.train_data.label_field
    else:
      self.label_field = 'label_ids'

  def build_model(self):
    if self.task_config.hub_module_url and self.task_config.init_checkpoint:
      raise ValueError('At most one of `hub_module_url` and '
                       '`init_checkpoint` can be specified.')
    if self.task_config.hub_module_url:
      encoder_network = utils.get_encoder_from_hub(
          self.task_config.hub_module_url)
    else:
      encoder_network = encoders.build_encoder(self.task_config.model.encoder)
    encoder_cfg = self.task_config.model.encoder.get()
    if self.task_config.model.encoder.type == 'xlnet':
      return models.XLNetClassifier(
          network=encoder_network,
          num_classes=self.task_config.model.num_classes,
          initializer=tf.keras.initializers.RandomNormal(
              stddev=encoder_cfg.initializer_range))
    else:
      return models.BertClassifier(
          network=encoder_network,
          num_classes=self.task_config.model.num_classes,
          initializer=tf.keras.initializers.TruncatedNormal(
              stddev=encoder_cfg.initializer_range),
          use_encoder_pooler=self.task_config.model.use_encoder_pooler)

  def build_losses(self, labels, model_outputs, aux_losses=None) -> tf.Tensor:
    label_ids = labels[self.label_field]
    if self.task_config.model.num_classes == 1:
      loss = tf.keras.losses.mean_squared_error(label_ids, model_outputs)
    else:
      loss = tf.keras.losses.sparse_categorical_crossentropy(
          label_ids, tf.cast(model_outputs, tf.float32), from_logits=True)

    if aux_losses:
      loss += tf.add_n(aux_losses)
    return tf_utils.safe_mean(loss)

  def build_inputs(self, params, input_context=None):
    """Returns tf.data.Dataset for sentence_prediction task."""
    if params.input_path == 'dummy':

      def dummy_data(_):
        dummy_ids = tf.zeros((1, params.seq_length), dtype=tf.int32)
        x = dict(
            input_word_ids=dummy_ids,
            input_mask=dummy_ids,
            input_type_ids=dummy_ids)

        if self.task_config.model.num_classes == 1:
          y = tf.zeros((1,), dtype=tf.float32)
        else:
          y = tf.zeros((1, 1), dtype=tf.int32)
        x[self.label_field] = y
        return x

      dataset = tf.data.Dataset.range(1)
      dataset = dataset.repeat()
      dataset = dataset.map(
          dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
      return dataset

    return data_loader_factory.get_data_loader(params).load(input_context)

  def build_metrics(self, training=None):
    del training
    if self.task_config.model.num_classes == 1:
      metrics = [tf.keras.metrics.MeanSquaredError()]
    else:
      metrics = [
          tf.keras.metrics.SparseCategoricalAccuracy(name='cls_accuracy')
      ]
    return metrics

  def process_metrics(self, metrics, labels, model_outputs):
    for metric in metrics:
      metric.update_state(labels[self.label_field], model_outputs)

  def process_compiled_metrics(self, compiled_metrics, labels, model_outputs):
    compiled_metrics.update_state(labels[self.label_field], model_outputs)

  def validation_step(self, inputs, model: tf.keras.Model, metrics=None):
    if self.metric_type == 'accuracy':
      return super(SentencePredictionTask,
                   self).validation_step(inputs, model, metrics)
    features, labels = inputs, inputs
    outputs = self.inference_step(features, model)
    loss = self.build_losses(
        labels=labels, model_outputs=outputs, aux_losses=model.losses)
    logs = {self.loss: loss}
    if self.metric_type == 'matthews_corrcoef':
      logs.update({
          'sentence_prediction':  # Ensure one prediction along batch dimension.
              tf.expand_dims(tf.math.argmax(outputs, axis=1), axis=1),
          'labels':
              labels[self.label_field],
      })
    if self.metric_type == 'pearson_spearman_corr':
      logs.update({
          'sentence_prediction': outputs,
          'labels': labels[self.label_field],
      })
    return logs

  def aggregate_logs(self, state=None, step_outputs=None):
    if self.metric_type == 'accuracy':
      return None
    if state is None:
      state = {'sentence_prediction': [], 'labels': []}
    state['sentence_prediction'].append(
        np.concatenate([v.numpy() for v in step_outputs['sentence_prediction']],
                       axis=0))
    state['labels'].append(
        np.concatenate([v.numpy() for v in step_outputs['labels']], axis=0))
    return state

  def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
    if self.metric_type == 'accuracy':
      return None
    elif self.metric_type == 'matthews_corrcoef':
      preds = np.concatenate(aggregated_logs['sentence_prediction'], axis=0)
      preds = np.reshape(preds, -1)
      labels = np.concatenate(aggregated_logs['labels'], axis=0)
      labels = np.reshape(labels, -1)
      return {
          self.metric_type: sklearn_metrics.matthews_corrcoef(preds, labels)
      }
    elif self.metric_type == 'pearson_spearman_corr':
      preds = np.concatenate(aggregated_logs['sentence_prediction'], axis=0)
      preds = np.reshape(preds, -1)
      labels = np.concatenate(aggregated_logs['labels'], axis=0)
      labels = np.reshape(labels, -1)
      pearson_corr = stats.pearsonr(preds, labels)[0]
      spearman_corr = stats.spearmanr(preds, labels)[0]
      corr_metric = (pearson_corr + spearman_corr) / 2
      return {self.metric_type: corr_metric}

  def initialize(self, model):
    """Load a pretrained checkpoint (if exists) and then train from iter 0."""
    ckpt_dir_or_file = self.task_config.init_checkpoint
    if not ckpt_dir_or_file:
      return
    if tf.io.gfile.isdir(ckpt_dir_or_file):
      ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)

    pretrain2finetune_mapping = {
        'encoder': model.checkpoint_items['encoder'],
    }
    if self.task_config.init_cls_pooler:
      # This option is valid when use_encoder_pooler is false.
      pretrain2finetune_mapping[
          'next_sentence.pooler_dense'] = model.checkpoint_items[
              'sentence_prediction.pooler_dense']
    ckpt = tf.train.Checkpoint(**pretrain2finetune_mapping)
    status = ckpt.read(ckpt_dir_or_file)
    status.expect_partial().assert_existing_objects_matched()
    logging.info('Finished loading pretrained checkpoint from %s',
                 ckpt_dir_or_file)


def predict(task: SentencePredictionTask,
            params: cfg.DataConfig,
            model: tf.keras.Model,
            params_aug: Optional[cfg.DataConfig] = None,
            test_time_aug_wgt: float = 0.3) -> List[Union[int, float]]:
  """Predicts on the input data.

  Args:
    task: A `SentencePredictionTask` object.
    params: A `cfg.DataConfig` object.
    model: A keras.Model.
    params_aug: A `cfg.DataConfig` object for augmented data.
    test_time_aug_wgt: Test time augmentation weight. The prediction score will
      use (1. - test_time_aug_wgt) original prediction plus test_time_aug_wgt
      augmented prediction.

  Returns:
    A list of predictions with length of `num_examples`. For regression task,
      each element in the list is the predicted score; for classification task,
      each element is the predicted class id.
  """

  def predict_step(inputs):
    """Replicated prediction calculation."""
    x = inputs
    example_id = x.pop('example_id')
    outputs = task.inference_step(x, model)
    return dict(example_id=example_id, predictions=outputs)

  def aggregate_fn(state, outputs):
    """Concatenates model's outputs."""
    if state is None:
      state = []

    for per_replica_example_id, per_replica_batch_predictions in zip(
        outputs['example_id'], outputs['predictions']):
      state.extend(zip(per_replica_example_id, per_replica_batch_predictions))
    return state

  dataset = orbit.utils.make_distributed_dataset(tf.distribute.get_strategy(),
                                                 task.build_inputs, params)
  outputs = utils.predict(predict_step, aggregate_fn, dataset)

  # When running on TPU POD, the order of output cannot be maintained,
  # so we need to sort by example_id.
  outputs = sorted(outputs, key=lambda x: x[0])
  is_regression = task.task_config.model.num_classes == 1
  if params_aug is not None:
    dataset_aug = orbit.utils.make_distributed_dataset(
        tf.distribute.get_strategy(), task.build_inputs, params_aug)
    outputs_aug = utils.predict(predict_step, aggregate_fn, dataset_aug)
    outputs_aug = sorted(outputs_aug, key=lambda x: x[0])
    if is_regression:
      return [(1. - test_time_aug_wgt) * x[1] + test_time_aug_wgt * y[1]
              for x, y in zip(outputs, outputs_aug)]
    else:
      return [
          tf.argmax(
              (1. - test_time_aug_wgt) * x[1] + test_time_aug_wgt * y[1],
              axis=-1) for x, y in zip(outputs, outputs_aug)
      ]
  if is_regression:
    return [x[1] for x in outputs]
  else:
    return [tf.argmax(x[1], axis=-1) for x in outputs]