class Config(_NewTask.Config):
tasks: Dict[str, NewTask.Config] = {}
task_weights: Dict[str, float] = {}
target_task_name: Optional[str] = None # for selecting best epoch
data: DisjointMultitaskData.Config = DisjointMultitaskData.Config()
metric_reporter: DisjointMultitaskMetricReporter.Config = (
DisjointMultitaskMetricReporter.Config())
def from_config(cls, task_config, metadata=None, model_state=None):
datas = OrderedDict()
models = OrderedDict()
metric_reporters = OrderedDict()
for name, task in task_config.tasks.items():
tensorizers, data = NewTask._init_tensorizers(task)
datas[name] = data
models[name] = NewTask._init_model(task, tensorizers)
metric_reporters[name] = create_component(
ComponentType.METRIC_REPORTER,
task.metric_reporter,
tensorizers=tensorizers,
)
task_weights = {
task_name: task_config.task_weights.get(task_name, 1.0)
for task_name in task_config.tasks.keys()
}
data = DisjointMultitaskData(task_config.data, datas)
model = NewDisjointMultitaskModel(models, loss_weights=task_weights)
if model_state:
model.load_state_dict(model_state)
metric_reporter = DisjointMultitaskMetricReporter(
metric_reporters,
loss_weights=task_weights,
target_task_name=task_config.target_task_name,
use_subtask_select_metric=(
task_config.metric_reporter.use_subtask_select_metric
),
)
trainer = create_trainer(task_config.trainer, model)
return cls(data, model, metric_reporter, trainer)
def from_config(
cls,
task_config: Config,
unused_metadata=None,
model_state=None,
tensorizers=None,
rank=0,
world_size=1,
):
data_dict = OrderedDict()
models = OrderedDict()
metric_reporters = OrderedDict()
tensorizers_dict = tensorizers or {}
# We can't really re-use the tensorizers, and the tensorizers saved for disjoint
# multitask are an empty dict right now anyway. Really we should serialize
# all of the subtasks individually.
for name, task in task_config.tasks.items():
tensorizers, data = cls._init_tensorizers(
task, tensorizers_dict.get(name), rank, world_size
)
data_dict[name] = data
models[name] = NewTask._init_model(task.model, tensorizers)
metric_reporters[name] = create_component(
ComponentType.METRIC_REPORTER,
task.metric_reporter,
tensorizers=tensorizers,
)
task_weights = {
task_name: task_config.task_weights.get(task_name, 1.0)
for task_name in task_config.tasks.keys()
}
data = DisjointMultitaskData.from_config(
task_config.data, data_dict=data_dict, rank=rank, world_size=world_size
)
# for serialization
data.tensorizers = {
name: data.data_dict[name].tensorizers for name in data.data_dict
}
model = NewDisjointMultitaskModel(models, loss_weights=task_weights)
if model_state:
model.load_state_dict(model_state)
metric_reporter = DisjointMultitaskMetricReporter(
metric_reporters,
loss_weights=task_weights,
target_task_name=task_config.target_task_name,
use_subtask_select_metric=(
task_config.metric_reporter.use_subtask_select_metric
),
)
trainer = create_trainer(task_config.trainer, model)
return cls(data, model, metric_reporter, trainer)