official/projects/fffner/fffner_prediction.py
# 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.
"""FFFNER prediction task."""
import collections
import dataclasses
from absl import logging
import numpy as np
import tensorflow as tf, tf_keras
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.tasks import utils
from official.projects.fffner import fffner_classifier
METRIC_TYPES = frozenset(
['accuracy', 'matthews_corrcoef', 'pearson_spearman_corr'])
@dataclasses.dataclass
class FFFNerModelConfig(base_config.Config):
"""A classifier/regressor configuration."""
num_classes_is_entity: int = 0
num_classes_entity_type: int = 0
use_encoder_pooler: bool = True
encoder: encoders.EncoderConfig = dataclasses.field(
default_factory=encoders.EncoderConfig
)
@dataclasses.dataclass
class FFFNerPredictionConfig(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: FFFNerModelConfig = dataclasses.field(
default_factory=FFFNerModelConfig
)
train_data: cfg.DataConfig = dataclasses.field(default_factory=cfg.DataConfig)
validation_data: cfg.DataConfig = dataclasses.field(
default_factory=cfg.DataConfig
)
@task_factory.register_task_cls(FFFNerPredictionConfig)
class FFFNerTask(base_task.Task):
"""Task object for FFFNer."""
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
self.label_field_is_entity = 'is_entity_label'
self.label_field_entity_type = 'entity_type_label'
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':
assert False, 'Not supported yet'
else:
return fffner_classifier.FFFNerClassifier(
# encoder_network.inputs
network=encoder_network,
num_classes_is_entity=self.task_config.model.num_classes_is_entity,
num_classes_entity_type=self.task_config.model
.num_classes_entity_type,
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_is_entity = labels[self.label_field_is_entity]
label_ids_entity_type = labels[self.label_field_entity_type]
loss_is_entity = tf_keras.losses.sparse_categorical_crossentropy(
label_ids_is_entity,
tf.cast(model_outputs[0], tf.float32),
from_logits=True)
loss_entity_type = tf_keras.losses.sparse_categorical_crossentropy(
label_ids_entity_type,
tf.cast(model_outputs[1], tf.float32),
from_logits=True)
loss = loss_is_entity + loss_entity_type
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,
is_entity_token_pos=tf.zeros((1, 1), dtype=tf.int32),
entity_type_token_pos=tf.ones((1, 1), dtype=tf.int32))
x[self.label_field_is_entity] = tf.zeros((1, 1), dtype=tf.int32)
x[self.label_field_entity_type] = tf.zeros((1, 1), dtype=tf.int32)
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
metrics = [
tf_keras.metrics.SparseCategoricalAccuracy(
name='cls_accuracy_is_entity'),
tf_keras.metrics.SparseCategoricalAccuracy(
name='cls_accuracy_entity_type'),
]
return metrics
def process_metrics(self, metrics, labels, model_outputs):
for metric in metrics:
if metric.name == 'cls_accuracy_is_entity':
metric.update_state(labels[self.label_field_is_entity],
model_outputs[0])
if metric.name == 'cls_accuracy_entity_type':
metric.update_state(labels[self.label_field_entity_type],
model_outputs[1])
def process_compiled_metrics(self, compiled_metrics, labels, model_outputs):
compiled_metrics.update_state(labels[self.label_field_is_entity],
model_outputs[0])
compiled_metrics.update_state(labels[self.label_field_entity_type],
model_outputs[1])
def validation_step(self, inputs, model: tf_keras.Model, metrics=None):
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 metrics:
self.process_metrics(metrics, labels, outputs)
if model.compiled_metrics:
self.process_compiled_metrics(model.compiled_metrics, labels, outputs)
logs.update({m.name: m.result() for m in metrics or []})
logs.update({m.name: m.result() for m in model.metrics})
logs.update({
'sentence_prediction_is_entity': outputs[0],
'sentence_prediction_entity_type': outputs[1],
'labels_is_entity': labels[self.label_field_is_entity],
'labels_entity_type': labels[self.label_field_entity_type],
'id': labels['example_id'],
'sentence_id': labels['sentence_id'],
'span_start': labels['span_start'],
'span_end': labels['span_end']
})
return logs
def aggregate_logs(self, state=None, step_outputs=None):
if state is None:
state = {
'sentence_prediction_is_entity': [],
'sentence_prediction_entity_type': [],
'labels_is_entity': [],
'labels_entity_type': [],
'ids': [],
'sentence_id': [],
'span_start': [],
'span_end': []
}
state['sentence_prediction_is_entity'].append(
np.concatenate(
[v.numpy() for v in step_outputs['sentence_prediction_is_entity']],
axis=0))
state['sentence_prediction_entity_type'].append(
np.concatenate([
v.numpy() for v in step_outputs['sentence_prediction_entity_type']
],
axis=0))
state['labels_is_entity'].append(
np.concatenate([v.numpy() for v in step_outputs['labels_is_entity']],
axis=0))
state['labels_entity_type'].append(
np.concatenate([v.numpy() for v in step_outputs['labels_entity_type']],
axis=0))
state['ids'].append(
np.concatenate([v.numpy() for v in step_outputs['id']], axis=0))
state['sentence_id'].append(
np.concatenate([v.numpy() for v in step_outputs['sentence_id']],
axis=0))
state['span_start'].append(
np.concatenate([v.numpy() for v in step_outputs['span_start']], axis=0))
state['span_end'].append(
np.concatenate([v.numpy() for v in step_outputs['span_end']], axis=0))
return state
def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
sentence_prediction_is_entity = np.concatenate(
aggregated_logs['sentence_prediction_is_entity'], axis=0)
sentence_prediction_is_entity = np.reshape(
sentence_prediction_is_entity,
(-1, self.task_config.model.num_classes_is_entity))
sentence_prediction_entity_type = np.concatenate(
aggregated_logs['sentence_prediction_entity_type'], axis=0)
sentence_prediction_entity_type = np.reshape(
sentence_prediction_entity_type,
(-1, self.task_config.model.num_classes_entity_type))
labels_is_entity = np.concatenate(
aggregated_logs['labels_is_entity'], axis=0)
labels_is_entity = np.reshape(labels_is_entity, -1)
labels_entity_type = np.concatenate(
aggregated_logs['labels_entity_type'], axis=0)
labels_entity_type = np.reshape(labels_entity_type, -1)
ids = np.concatenate(aggregated_logs['ids'], axis=0)
ids = np.reshape(ids, -1)
sentence_id = np.concatenate(aggregated_logs['sentence_id'], axis=0)
sentence_id = np.reshape(sentence_id, -1)
span_start = np.concatenate(aggregated_logs['span_start'], axis=0)
span_start = np.reshape(span_start, -1)
span_end = np.concatenate(aggregated_logs['span_end'], axis=0)
span_end = np.reshape(span_end, -1)
def resolve(length, spans, prediction_confidence):
used = [False] * length
spans = sorted(
spans,
key=lambda x: prediction_confidence[(x[0], x[1])],
reverse=True)
real_spans = []
for span_start, span_end, ent_type in spans:
fill = False
for s in range(span_start, span_end + 1):
if used[s]:
fill = True
break
if not fill:
real_spans.append((span_start, span_end, ent_type))
for s in range(span_start, span_end + 1):
used[s] = True
return real_spans
def get_p_r_f(truth, pred):
n_pred = len(pred)
n_truth = len(truth)
n_correct = len(set(pred) & set(truth))
precision = 1. * n_correct / n_pred if n_pred != 0 else 0.0
recall = 1. * n_correct / n_truth if n_truth != 0 else 0.0
f1 = 2 * precision * recall / (
precision + recall) if precision + recall != 0.0 else 0.0
return {
'n_pred': n_pred,
'n_truth': n_truth,
'n_correct': n_correct,
'precision': precision,
'recall': recall,
'f1': f1,
}
def softmax(x):
x = np.array(x)
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum(axis=0)
per_sid_results = collections.defaultdict(list)
for _, sent_id, sp_start, sp_end, is_entity_label, is_entity_logit, entity_type_label, entity_type_logit in zip(
ids, sentence_id, span_start, span_end, labels_is_entity,
sentence_prediction_is_entity, labels_entity_type,
sentence_prediction_entity_type):
if sent_id > 0:
per_sid_results[sent_id].append(
(sp_start, sp_end, is_entity_label, is_entity_logit,
entity_type_label, entity_type_logit))
ground_truth = []
prediction_is_entity = []
prediction_entity_type = []
for key in sorted(list(per_sid_results.keys())):
results = per_sid_results[key]
gt_entities = []
predictied_entities = []
prediction_confidence = {}
prediction_confidence_type = {}
length = 0
for span_start, span_end, ground_truth_span, prediction_span, ground_truth_type, prediction_type in results:
if ground_truth_span == 1:
gt_entities.append((span_start, span_end, ground_truth_type))
if prediction_span[1] > prediction_span[0]:
predictied_entities.append(
(span_start, span_end, np.argmax(prediction_type).item()))
prediction_confidence[(span_start,
span_end)] = max(softmax(prediction_span))
prediction_confidence_type[(span_start,
span_end)] = max(softmax(prediction_type))
length = max(length, span_end)
length += 1
ground_truth.extend([(key, *x) for x in gt_entities])
prediction_is_entity.extend([(key, *x) for x in predictied_entities])
resolved_predicted = resolve(length, predictied_entities,
prediction_confidence)
prediction_entity_type.extend([(key, *x) for x in resolved_predicted])
raw = get_p_r_f(ground_truth, prediction_is_entity)
resolved = get_p_r_f(ground_truth, prediction_entity_type)
return {
'raw_f1': raw['f1'],
'raw_precision': raw['precision'],
'raw_recall': raw['recall'],
'resolved_f1': resolved['f1'],
'resolved_precision': resolved['precision'],
'resolved_recall': resolved['recall'],
'overall_f1': raw['f1'] + resolved['f1'],
}
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
logging.info('Trying to load pretrained checkpoint from %s',
ckpt_dir_or_file)
if ckpt_dir_or_file and tf.io.gfile.isdir(ckpt_dir_or_file):
ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)
if not ckpt_dir_or_file:
logging.info('No checkpoint file found from %s. Will not load.',
ckpt_dir_or_file)
return
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)