def _format_precision_config(self):
amp_type = self.lightning_module.trainer.accelerator_connector.amp_type
amp_level = self.lightning_module.trainer.accelerator_connector.amp_level
precision = self.lightning_module.trainer.accelerator_connector.precision
if precision == 16:
if "fp16" not in self.config and amp_type == AMPType.NATIVE:
# FP16 is a DeepSpeed standalone AMP implementation
rank_zero_info("Enabling DeepSpeed FP16.")
self.config["fp16"] = {
"enabled": True,
"loss_scale": self.loss_scale,
"initial_scale_power": self.initial_scale_power,
"loss_scale_window": self.loss_scale_window,
"hysteresis": self.hysteresis,
"min_loss_scale": self.min_loss_scale
}
elif "amp" not in self.config and amp_type == AMPType.APEX:
rank_zero_only("Enabling DeepSpeed APEX Implementation.")
self.config["amp"] = {
"enabled": True,
"opt_level": amp_level,
}
if "zero_optimization" in self.config and not ("amp" in self.config or
"fp16" in self.config):
raise MisconfigurationException(
"To use DeepSpeed ZeRO Optimization, you must set precision=16."
)
def __new__(cls, *args: Any, **kwargs: Any) -> 'LightningDataModule':
obj = super().__new__(cls)
# track `DataHooks` calls and run `prepare_data` only on rank zero
obj.prepare_data = cls._track_data_hook_calls(
obj, rank_zero_only(obj.prepare_data))
obj.setup = cls._track_data_hook_calls(obj, obj.setup)
obj.teardown = cls._track_data_hook_calls(obj, obj.teardown)
return obj
def _format_precision_config(self):
if self.amp_type == AMPType.APEX:
amp_level = self.amp_level
if self.precision in (16, "mixed"):
if "fp16" not in self.config and self.amp_type == AMPType.NATIVE:
# FP16 is a DeepSpeed standalone AMP implementation
rank_zero_info("Enabling DeepSpeed FP16.")
self.config["fp16"] = {
"enabled": True,
"loss_scale": self.loss_scale,
"initial_scale_power": self.initial_scale_power,
"loss_scale_window": self.loss_scale_window,
"hysteresis": self.hysteresis,
"min_loss_scale": self.min_loss_scale,
}
elif "amp" not in self.config and self.amp_type == AMPType.APEX:
rank_zero_only("Enabling DeepSpeed APEX Implementation.")
self.config["amp"] = {"enabled": True, "opt_level": amp_level}
def _format_precision_config(self):
amp_type = self.lightning_module.trainer.accelerator_connector.amp_type
amp_level = self.lightning_module.trainer.accelerator_connector.amp_level
precision = self.lightning_module.trainer.accelerator_connector.precision
if precision in (16, 'mixed'):
if "fp16" not in self.config and amp_type == AMPType.NATIVE:
# FP16 is a DeepSpeed standalone AMP implementation
rank_zero_info("Enabling DeepSpeed FP16.")
self.config["fp16"] = {
"enabled": True,
"loss_scale": self.loss_scale,
"initial_scale_power": self.initial_scale_power,
"loss_scale_window": self.loss_scale_window,
"hysteresis": self.hysteresis,
"min_loss_scale": self.min_loss_scale
}
elif "amp" not in self.config and amp_type == AMPType.APEX:
rank_zero_only("Enabling DeepSpeed APEX Implementation.")
self.config["amp"] = {
"enabled": True,
"opt_level": amp_level,
}
parser.add_argument("--num_eval_passages", type=int, default=100)
parser.add_argument("--eval_batch_size", default=4, type=int)
parser.add_argument("--target_dataset",
default="test",
choices=["validation", "test"])
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
logging.basicConfig(
level=logging.WARN,
format=
"[%(asctime)s] [%(levelname)s] %(message)s (%(funcName)s@%(filename)s:%(lineno)s)"
)
logging.getLogger("lightning").setLevel(logging.ERROR)
# Reader.prepare_data = lambda self: None
model = Reader.load_from_checkpoint(args.checkpoint_file)
model.hparams.num_eval_passages = args.num_eval_passages
model.hparams.eval_batch_size = args.eval_batch_size
if args.target_dataset == "validation":
model.hparams.test_file = model.hparams.validation_file
model.hparams.nq_gold_test_file = model.hparams.nq_gold_validation_file
trainer = Trainer.from_argparse_args(args)
result = trainer.test(model)
def report_results():
print("result: %s" % result)
rank_zero_only(report_results)()
