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import os
from dataclasses import dataclass, field
import pytorch_lightning as pl
import torch.nn.functional as F
import craftsman
from craftsman.utils.base import (
Updateable,
update_end_if_possible,
update_if_possible,
)
from craftsman.utils.scheduler import parse_optimizer, parse_scheduler
from craftsman.utils.config import parse_structured
from craftsman.utils.misc import C, cleanup, get_device, load_module_weights
from craftsman.utils.saving import SaverMixin
from craftsman.utils.typing import *
class BaseSystem(pl.LightningModule, Updateable, SaverMixin):
@dataclass
class Config:
loggers: dict = field(default_factory=dict)
loss: dict = field(default_factory=dict)
optimizer: dict = field(default_factory=dict)
scheduler: Optional[dict] = None
weights: Optional[str] = None
weights_ignore_modules: Optional[List[str]] = None
cleanup_after_validation_step: bool = False
cleanup_after_test_step: bool = False
pretrained_model_path: Optional[str] = None
strict_load: bool = True
cfg: Config
def __init__(self, cfg, resumed=False) -> None:
super().__init__()
self.cfg = parse_structured(self.Config, cfg)
self._save_dir: Optional[str] = None
self._resumed: bool = resumed
self._resumed_eval: bool = False
self._resumed_eval_status: dict = {"global_step": 0, "current_epoch": 0}
if "loggers" in cfg:
self.create_loggers(cfg.loggers)
self.configure()
if self.cfg.weights is not None:
self.load_weights(self.cfg.weights, self.cfg.weights_ignore_modules)
self.post_configure()
def load_weights(self, weights: str, ignore_modules: Optional[List[str]] = None):
state_dict, epoch, global_step = load_module_weights(
weights, ignore_modules=ignore_modules, map_location="cpu"
)
self.load_state_dict(state_dict, strict=False)
# restore step-dependent states
self.do_update_step(epoch, global_step, on_load_weights=True)
def set_resume_status(self, current_epoch: int, global_step: int):
# restore correct epoch and global step in eval
self._resumed_eval = True
self._resumed_eval_status["current_epoch"] = current_epoch
self._resumed_eval_status["global_step"] = global_step
@property
def resumed(self):
# whether from resumed checkpoint
return self._resumed
@property
def true_global_step(self):
if self._resumed_eval:
return self._resumed_eval_status["global_step"]
else:
return self.global_step
@property
def true_current_epoch(self):
if self._resumed_eval:
return self._resumed_eval_status["current_epoch"]
else:
return self.current_epoch
def configure(self) -> None:
pass
def post_configure(self) -> None:
"""
executed after weights are loaded
"""
pass
def C(self, value: Any) -> float:
return C(value, self.true_current_epoch, self.true_global_step)
def configure_optimizers(self):
optim = parse_optimizer(self.cfg.optimizer, self)
ret = {
"optimizer": optim,
}
if self.cfg.scheduler is not None:
ret.update(
{
"lr_scheduler": parse_scheduler(self.cfg.scheduler, optim),
}
)
return ret
def training_step(self, batch, batch_idx):
raise NotImplementedError
def validation_step(self, batch, batch_idx):
raise NotImplementedError
def on_train_batch_end(self, outputs, batch, batch_idx):
self.dataset = self.trainer.train_dataloader.dataset
update_end_if_possible(
self.dataset, self.true_current_epoch, self.true_global_step
)
self.do_update_step_end(self.true_current_epoch, self.true_global_step)
def on_validation_batch_end(self, outputs, batch, batch_idx):
self.dataset = self.trainer.val_dataloaders.dataset
update_end_if_possible(
self.dataset, self.true_current_epoch, self.true_global_step
)
self.do_update_step_end(self.true_current_epoch, self.true_global_step)
if self.cfg.cleanup_after_validation_step:
# cleanup to save vram
cleanup()
def on_validation_epoch_end(self):
raise NotImplementedError
def test_step(self, batch, batch_idx):
raise NotImplementedError
def on_test_batch_end(self, outputs, batch, batch_idx):
self.dataset = self.trainer.test_dataloaders.dataset
update_end_if_possible(
self.dataset, self.true_current_epoch, self.true_global_step
)
self.do_update_step_end(self.true_current_epoch, self.true_global_step)
if self.cfg.cleanup_after_test_step:
# cleanup to save vram
cleanup()
def on_test_epoch_end(self):
pass
def predict_step(self, batch, batch_idx):
raise NotImplementedError
def on_predict_batch_end(self, outputs, batch, batch_idx):
self.dataset = self.trainer.predict_dataloaders.dataset
update_end_if_possible(
self.dataset, self.true_current_epoch, self.true_global_step
)
self.do_update_step_end(self.true_current_epoch, self.true_global_step)
if self.cfg.cleanup_after_test_step:
# cleanup to save vram
cleanup()
def on_predict_epoch_end(self):
pass
def preprocess_data(self, batch, stage):
pass
"""
Implementing on_after_batch_transfer of DataModule does the same.
But on_after_batch_transfer does not support DP.
"""
def on_train_batch_start(self, batch, batch_idx, unused=0):
self.preprocess_data(batch, "train")
self.dataset = self.trainer.train_dataloader.dataset
update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
self.do_update_step(self.true_current_epoch, self.true_global_step)
def on_validation_batch_start(self, batch, batch_idx, dataloader_idx=0):
self.preprocess_data(batch, "validation")
self.dataset = self.trainer.val_dataloaders.dataset
update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
self.do_update_step(self.true_current_epoch, self.true_global_step)
def on_test_batch_start(self, batch, batch_idx, dataloader_idx=0):
self.preprocess_data(batch, "test")
self.dataset = self.trainer.test_dataloaders.dataset
update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
self.do_update_step(self.true_current_epoch, self.true_global_step)
def on_predict_batch_start(self, batch, batch_idx, dataloader_idx=0):
self.preprocess_data(batch, "predict")
self.dataset = self.trainer.predict_dataloaders.dataset
update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
self.do_update_step(self.true_current_epoch, self.true_global_step)
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
pass
def on_before_optimizer_step(self, optimizer):
"""
# some gradient-related debugging goes here, example:
from lightning.pytorch.utilities import grad_norm
norms = grad_norm(self.geometry, norm_type=2)
print(norms)
"""
pass
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