def create(
config: Config,
dataset: Dataset,
configuration_key: str,
vocab_size: int,
init_for_load_only=False,
parameter_client=None,
lapse_offset=0,
complete_vocab_size=None,
) -> "KgeEmbedder":
"""Factory method for embedder creation."""
if complete_vocab_size is None:
complete_vocab_size = vocab_size
try:
embedder_type = config.get_default(configuration_key + ".type")
class_name = config.get(embedder_type + ".class_name")
except:
raise Exception("Can't find {}.type in config".format(configuration_key))
try:
if "distributed" in config.get("train.type"):
class_name = "Distributed" + class_name
embedder = init_from(
class_name,
config.get("modules"),
config=config,
dataset=dataset,
configuration_key=configuration_key,
vocab_size=vocab_size,
init_for_load_only=init_for_load_only,
parameter_client=parameter_client,
lapse_offset=lapse_offset,
complete_vocab_size=complete_vocab_size
)
else:
embedder = init_from(
class_name,
config.get("modules"),
config,
dataset,
configuration_key,
vocab_size,
init_for_load_only=init_for_load_only,
)
return embedder
except:
config.log(f"Failed to create embedder {embedder_type} (class {class_name}).")
raise
def create(
config: Config,
dataset: Dataset,
configuration_key: Optional[str] = None,
init_for_load_only=False,
) -> "KgeModel":
"""Factory method for model creation."""
try:
if configuration_key is not None:
model_name = config.get(configuration_key + ".type")
else:
model_name = config.get("model")
config._import(model_name)
class_name = config.get(model_name + ".class_name")
except:
raise Exception("Can't find {}.type in config".format(configuration_key))
try:
model = init_from(
class_name,
config.get("modules"),
config=config,
dataset=dataset,
configuration_key=configuration_key,
init_for_load_only=init_for_load_only,
)
model.to(config.get("job.device"))
return model
except:
config.log(f"Failed to create model {model_name} (class {class_name}).")
raise
def create(
config: Config,
dataset: Dataset,
configuration_key: str,
vocab_size: int,
init_for_load_only=False,
) -> "KgeEmbedder":
"""Factory method for embedder creation."""
try:
embedder_type = config.get_default(configuration_key + ".type")
class_name = config.get(embedder_type + ".class_name")
except:
raise Exception("Can't find {}.type in config".format(configuration_key))
try:
embedder = init_from(
class_name,
config.get("modules"),
config,
dataset,
configuration_key,
vocab_size,
init_for_load_only=init_for_load_only,
)
return embedder
except:
config.log(
f"Failed to create embedder {embedder_type} (class {class_name})."
)
raise
def create(config, dataset, parent_job=None):
"""Factory method to create a search job."""
search_type = config.get("search.type")
class_name = config.get_default(f"{search_type}.class_name")
return init_from(class_name, config.modules(), config, dataset,
parent_job)
def create(
config: Config,
dataset: Dataset,
parent_job: Job = None,
model=None,
forward_only=False,
parameter_client=None,
init_for_load_only=False,
) -> "TrainingJob":
"""Factory method to create a training job."""
train_type = config.get("train.type")
class_name = config.get_default(f"{train_type}.class_name")
job_config_object = {
"class_name": class_name,
"modules": config.modules(),
"config": config,
"dataset": dataset,
"parent_job": parent_job,
"model": model,
"forward_only": forward_only,
}
if "distributed" in train_type:
job_config_object.update({
"parameter_client": parameter_client,
"init_for_load_only": init_for_load_only
})
return init_from(**job_config_object)
def create(config, dataset, parent_job=None, model=None):
"""Factory method to create an evaluation job """
eval_type = config.get("eval.type")
class_name = config.get_default(f"{eval_type}.class_name")
return init_from(
class_name,
config.modules(),
config,
dataset,
parent_job=parent_job,
model=model,
)
def create(
config: Config,
dataset: Dataset,
parent_job: Job = None,
model=None,
forward_only=False,
) -> "TrainingJob":
"""Factory method to create a training job."""
train_type = config.get("train.type")
class_name = config.get_default(f"{train_type}.class_name")
return init_from(
class_name,
config.modules(),
config,
dataset,
parent_job,
model=model,
forward_only=forward_only,
)
