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# 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.
# ==============================================================================
"""Masked language task."""
import dataclasses
import tensorflow as tf
from official.core import base_task
from official.modeling.hyperparams import config_definitions as cfg
from official.nlp.configs import bert
from official.nlp.data import pretrain_dataloader
from official.nlp.modeling import losses as loss_lib
@dataclasses.dataclass
class MaskedLMConfig(cfg.TaskConfig):
"""The model config."""
network: bert.BertPretrainerConfig = bert.BertPretrainerConfig(cls_heads=[
bert.ClsHeadConfig(
inner_dim=768, num_classes=2, dropout_rate=0.1, name='next_sentence')
])
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
@base_task.register_task_cls(MaskedLMConfig)
class MaskedLMTask(base_task.Task):
"""Mock task object for testing."""
def build_model(self):
return bert.instantiate_bertpretrainer_from_cfg(self.task_config.network)
def build_losses(self,
labels,
model_outputs,
metrics,
aux_losses=None) -> tf.Tensor:
metrics = dict([(metric.name, metric) for metric in metrics])
lm_output = tf.nn.log_softmax(
tf.cast(model_outputs['lm_output'], tf.float32), axis=-1)
mlm_loss = loss_lib.weighted_sparse_categorical_crossentropy_loss(
labels=labels['masked_lm_ids'],
predictions=lm_output,
weights=labels['masked_lm_weights'])
metrics['lm_example_loss'].update_state(mlm_loss)
if 'next_sentence_labels' in labels:
sentence_labels = labels['next_sentence_labels']
sentence_outputs = tf.cast(
model_outputs['next_sentence'], dtype=tf.float32)
sentence_loss = loss_lib.weighted_sparse_categorical_crossentropy_loss(
labels=sentence_labels,
predictions=tf.nn.log_softmax(sentence_outputs, axis=-1))
metrics['next_sentence_loss'].update_state(sentence_loss)
total_loss = mlm_loss + sentence_loss
else:
total_loss = mlm_loss
if aux_losses:
total_loss += tf.add_n(aux_losses)
return total_loss
def build_inputs(self, params, input_context=None):
"""Returns tf.data.Dataset for pretraining."""
if params.input_path == 'dummy':
def dummy_data(_):
dummy_ids = tf.zeros((1, params.seq_length), dtype=tf.int32)
dummy_lm = tf.zeros((1, params.max_predictions_per_seq), dtype=tf.int32)
return dict(
input_word_ids=dummy_ids,
input_mask=dummy_ids,
input_type_ids=dummy_ids,
masked_lm_positions=dummy_lm,
masked_lm_ids=dummy_lm,
masked_lm_weights=tf.cast(dummy_lm, dtype=tf.float32),
next_sentence_labels=tf.zeros((1, 1), dtype=tf.int32))
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset
return pretrain_dataloader.BertPretrainDataLoader(params).load(
input_context)
def build_metrics(self, training=None):
del training
metrics = [
tf.keras.metrics.SparseCategoricalAccuracy(name='masked_lm_accuracy'),
tf.keras.metrics.Mean(name='lm_example_loss')
]
# TODO(hongkuny): rethink how to manage metrics creation with heads.
if self.task_config.train_data.use_next_sentence_label:
metrics.append(
tf.keras.metrics.SparseCategoricalAccuracy(
name='next_sentence_accuracy'))
metrics.append(tf.keras.metrics.Mean(name='next_sentence_loss'))
return metrics
def process_metrics(self, metrics, labels, model_outputs):
metrics = dict([(metric.name, metric) for metric in metrics])
if 'masked_lm_accuracy' in metrics:
metrics['masked_lm_accuracy'].update_state(labels['masked_lm_ids'],
model_outputs['lm_output'],
labels['masked_lm_weights'])
if 'next_sentence_accuracy' in metrics:
metrics['next_sentence_accuracy'].update_state(
labels['next_sentence_labels'], model_outputs['next_sentence'])
def train_step(self, inputs, model: tf.keras.Model,
optimizer: tf.keras.optimizers.Optimizer, metrics):
"""Does forward and backward.
Args:
inputs: a dictionary of input tensors.
model: the model, forward pass definition.
optimizer: the optimizer for this training step.
metrics: a nested structure of metrics objects.
Returns:
A dictionary of logs.
"""
with tf.GradientTape() as tape:
outputs = model(inputs, training=True)
# Computes per-replica loss.
loss = self.build_losses(
labels=inputs,
model_outputs=outputs,
metrics=metrics,
aux_losses=model.losses)
# Scales loss as the default gradients allreduce performs sum inside the
# optimizer.
# TODO(b/154564893): enable loss scaling.
# scaled_loss = loss / tf.distribute.get_strategy().num_replicas_in_sync
tvars = model.trainable_variables
grads = tape.gradient(loss, tvars)
optimizer.apply_gradients(list(zip(grads, tvars)))
self.process_metrics(metrics, inputs, outputs)
return {self.loss: loss}
def validation_step(self, inputs, model: tf.keras.Model, metrics):
"""Validatation step.
Args:
inputs: a dictionary of input tensors.
model: the keras.Model.
metrics: a nested structure of metrics objects.
Returns:
A dictionary of logs.
"""
outputs = self.inference_step(inputs, model)
loss = self.build_losses(
labels=inputs,
model_outputs=outputs,
metrics=metrics,
aux_losses=model.losses)
self.process_metrics(metrics, inputs, outputs)
return {self.loss: loss}
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