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fx_validator.py
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fx_validator.py
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# Copyright The PyTorch Lightning team.
#
# 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.
from typing import Optional, Tuple, Union
from typing_extensions import TypedDict
from pytorch_lightning.utilities.exceptions import MisconfigurationException
class _FxValidator:
class _LogOptions(TypedDict):
allowed_on_step: Union[Tuple[bool], Tuple[bool, bool]]
allowed_on_epoch: Union[Tuple[bool], Tuple[bool, bool]]
default_on_step: bool
default_on_epoch: bool
functions = {
"on_before_accelerator_backend_setup": None,
"on_configure_sharded_model": None,
"on_before_backward": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"backward": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"on_after_backward": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"on_before_optimizer_step": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"optimizer_step": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"lr_scheduler_step": None,
"on_before_zero_grad": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"optimizer_zero_grad": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"on_init_start": None,
"on_init_end": None,
"on_fit_start": None,
"on_fit_end": None,
"on_sanity_check_start": None,
"on_sanity_check_end": None,
"on_train_start": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_train_end": None,
"on_validation_start": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_validation_end": None,
"on_test_start": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_test_end": None,
"on_predict_start": None,
"on_predict_end": None,
"on_pretrain_routine_start": None,
"on_pretrain_routine_end": None,
"on_train_epoch_start": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_train_epoch_end": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_validation_epoch_start": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_validation_epoch_end": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_test_epoch_start": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_test_epoch_end": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_predict_epoch_start": None,
"on_predict_epoch_end": None,
"on_epoch_start": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_epoch_end": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"on_batch_start": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"on_batch_end": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"on_train_batch_start": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"on_train_batch_end": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"on_validation_batch_start": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"on_validation_batch_end": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"on_test_batch_start": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"on_test_batch_end": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"on_predict_batch_start": None,
"on_predict_batch_end": None,
"on_keyboard_interrupt": None,
"on_exception": None,
"on_save_checkpoint": None,
"on_load_checkpoint": None,
"setup": None,
"teardown": None,
"configure_sharded_model": None,
"training_step": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"validation_step": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"test_step": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"predict_step": None,
"training_step_end": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
),
"validation_step_end": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"test_step_end": _LogOptions(
allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=False, default_on_epoch=True
),
"training_epoch_end": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"validation_epoch_end": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"test_epoch_end": _LogOptions(
allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
),
"configure_optimizers": None,
"on_train_dataloader": None,
"train_dataloader": None,
"on_val_dataloader": None,
"val_dataloader": None,
"on_test_dataloader": None,
"test_dataloader": None,
"prepare_data": None,
"configure_callbacks": None,
"on_validation_model_eval": None,
"on_test_model_eval": None,
"on_validation_model_train": None,
"on_test_model_train": None,
}
@classmethod
def check_logging(cls, fx_name: str) -> None:
"""Check if the given hook is allowed to log."""
if fx_name not in cls.functions:
raise RuntimeError(
f"Logging inside `{fx_name}` is not implemented."
" Please, open an issue in `https://github.com/PyTorchLightning/pytorch-lightning/issues`."
)
if cls.functions[fx_name] is None:
raise MisconfigurationException(f"You can't `self.log()` inside `{fx_name}`.")
@classmethod
def get_default_logging_levels(
cls, fx_name: str, on_step: Optional[bool], on_epoch: Optional[bool]
) -> Tuple[bool, bool]:
"""Return default logging levels for given hook."""
fx_config = cls.functions[fx_name]
assert fx_config is not None
on_step = fx_config["default_on_step"] if on_step is None else on_step
on_epoch = fx_config["default_on_epoch"] if on_epoch is None else on_epoch
return on_step, on_epoch
@classmethod
def check_logging_levels(cls, fx_name: str, on_step: bool, on_epoch: bool) -> None:
"""Check if the logging levels are allowed in the given hook."""
fx_config = cls.functions[fx_name]
assert fx_config is not None
m = "You can't `self.log({}={})` inside `{}`, must be one of {}."
if on_step not in fx_config["allowed_on_step"]:
msg = m.format("on_step", on_step, fx_name, fx_config["allowed_on_step"])
raise MisconfigurationException(msg)
if on_epoch not in fx_config["allowed_on_epoch"]:
msg = m.format("on_epoch", on_epoch, fx_name, fx_config["allowed_on_epoch"])
raise MisconfigurationException(msg)
@classmethod
def check_logging_and_get_default_levels(
cls, fx_name: str, on_step: Optional[bool], on_epoch: Optional[bool]
) -> Tuple[bool, bool]:
"""Check if the given hook name is allowed to log and return logging levels."""
cls.check_logging(fx_name)
on_step, on_epoch = cls.get_default_logging_levels(fx_name, on_step, on_epoch)
cls.check_logging_levels(fx_name, on_step, on_epoch)
return on_step, on_epoch