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# Copyright 2023 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.
"""Experimental MultiTask base class for multi-task training/evaluation."""
import abc
from typing import Dict, List, Optional, Text, Union
import tensorflow as tf, tf_keras
from official.core import base_task
from official.core import config_definitions
from official.core import task_factory
from official.modeling import optimization
from official.modeling.multitask import base_model
from official.modeling.multitask import configs
from official.modeling.privacy import configs as dp_configs
OptimizationConfig = optimization.OptimizationConfig
RuntimeConfig = config_definitions.RuntimeConfig
DifferentialPrivacyConfig = dp_configs.DifferentialPrivacyConfig
class MultiTask(tf.Module, metaclass=abc.ABCMeta):
"""A multi-task class to manage multiple tasks."""
def __init__(self,
tasks: Union[Dict[Text, base_task.Task], List[base_task.Task]],
task_weights: Optional[Dict[str, Union[float, int]]] = None,
task_eval_steps: Optional[Dict[str, int]] = None,
name: Optional[str] = None):
"""MultiTask initialization.
Args:
tasks: a list or a flat dict of Task.
task_weights: a dict of (task, task weight), task weight can be applied
directly during loss summation in a joint backward step, or it can be
used to sample task among interleaved backward step.
task_eval_steps: a dict of (task, eval steps).
name: the instance name of a MultiTask object.
"""
super().__init__(name=name)
if isinstance(tasks, list):
self._tasks = {}
for task in tasks:
if task.name in self._tasks:
raise ValueError("Duplicated tasks found, task.name is %s" %
task.name)
self._tasks[task.name] = task
elif isinstance(tasks, dict):
self._tasks = tasks
else:
raise ValueError("The tasks argument has an invalid type: %s" %
type(tasks))
self.task_eval_steps = task_eval_steps or {}
self._task_weights = task_weights or {}
self._task_weights = dict([
(name, self._task_weights.get(name, 1.0)) for name in self.tasks
])
@classmethod
def from_config(cls, config: configs.MultiTaskConfig, logging_dir=None):
tasks = {}
task_eval_steps = {}
task_weights = {}
for task_routine in config.task_routines:
task_name = task_routine.task_name or task_routine.task_config.name
tasks[task_name] = task_factory.get_task(
task_routine.task_config, logging_dir=logging_dir, name=task_name)
task_eval_steps[task_name] = task_routine.eval_steps
task_weights[task_name] = task_routine.task_weight
return cls(
tasks, task_eval_steps=task_eval_steps, task_weights=task_weights)
@property
def tasks(self):
return self._tasks
def task_weight(self, task_name):
return self._task_weights[task_name]
@property
def task_weights(self):
return self._task_weights
@classmethod
def create_optimizer(cls,
optimizer_config: OptimizationConfig,
runtime_config: Optional[RuntimeConfig] = None,
dp_config: Optional[DifferentialPrivacyConfig] = None):
return base_task.Task.create_optimizer(
optimizer_config=optimizer_config, runtime_config=runtime_config,
dp_config=dp_config)
def joint_train_step(self, task_inputs,
multi_task_model: base_model.MultiTaskBaseModel,
optimizer: tf_keras.optimizers.Optimizer, task_metrics,
**kwargs):
"""The joint train step.
Args:
task_inputs: a dictionary of task names and per-task features.
multi_task_model: a MultiTaskBaseModel instance.
optimizer: a tf.optimizers.Optimizer.
task_metrics: a dictionary of task names and per-task metrics.
**kwargs: other arguments to pass through.
Returns:
A dictionary of losses, inculding per-task losses and their weighted sum.
"""
losses = {}
with tf.GradientTape() as tape:
total_loss = 0.0
for name, model in multi_task_model.sub_tasks.items():
inputs = task_inputs[name]
if isinstance(inputs, tuple) and len(inputs) == 2:
features, labels = inputs
elif isinstance(inputs, dict):
features, labels = inputs, inputs
else:
raise ValueError("The iterator output is neither a tuple nor a "
"dictionary. It is not implemented to support "
"such outputs.")
outputs = model(features, training=True)
task_loss = self.tasks[name].build_losses(labels, outputs)
task_weight = self.task_weight(name)
total_loss += task_weight * task_loss
losses[name] = task_loss
self.tasks[name].process_metrics(task_metrics[name], labels, outputs,
**kwargs)
# Scales loss as the default gradients allreduce performs sum inside
# the optimizer.
scaled_loss = total_loss / tf.distribute.get_strategy(
).num_replicas_in_sync
tvars = multi_task_model.trainable_variables
grads = tape.gradient(scaled_loss, tvars)
optimizer.apply_gradients(list(zip(grads, tvars)))
losses["total_loss"] = total_loss
return losses
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