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import abc
import os
import pytorch_lightning as pl
from scripts.utils.lr_scheduler import Esm2LRScheduler
from torch import distributed as dist
class AbstractModel(pl.LightningModule):
def __init__(self,
lr_scheduler_kwargs: dict = None,
optimizer_kwargs: dict = None,
save_path: str = None,
from_checkpoint: str = None,
load_prev_scheduler: bool = False,
save_weights_only: bool = True,):
"""
Args:
lr_scheduler: Kwargs for lr_scheduler
optimizer_kwargs: Kwargs for optimizer_kwargs
save_path: Save trained model
from_checkpoint: Load model from checkpoint
load_prev_scheduler: Whether load previous scheduler from save_path
load_strict: Whether load model strictly
save_weights_only: Whether save only weights or also optimizer and lr_scheduler
"""
super().__init__()
self.initialize_model()
self.metrics = {}
for stage in ["train", "valid", "test"]:
stage_metrics = self.initialize_metrics(stage)
# Rigister metrics as attributes
for metric_name, metric in stage_metrics.items():
setattr(self, metric_name, metric)
self.metrics[stage] = stage_metrics
self.lr_scheduler_kwargs = {"init_lr": 0} if lr_scheduler_kwargs is None else lr_scheduler_kwargs
self.optimizer_kwargs = {} if optimizer_kwargs is None else optimizer_kwargs
self.init_optimizers()
self.save_path = save_path
self.save_weights_only = save_weights_only
self.step = 0
self.epoch = 0
self.load_prev_scheduler = load_prev_scheduler
if from_checkpoint:
self.load_checkpoint(from_checkpoint, load_prev_scheduler)
@abc.abstractmethod
def initialize_model(self) -> None:
"""
All model initialization should be done here
Note that the whole model must be named as "self.model" for model saving and loading
"""
raise NotImplementedError
@abc.abstractmethod
def forward(self, *args, **kwargs):
"""
Forward propagation
"""
raise NotImplementedError
@abc.abstractmethod
def initialize_metrics(self, stage: str) -> dict:
"""
Initialize metrics for each stage
Args:
stage: "train", "valid" or "test"
Returns:
A dictionary of metrics for the stage. Keys are metric names and values are metric objects
"""
raise NotImplementedError
@abc.abstractmethod
def loss_func(self, stage: str, outputs, labels) -> torch.Tensor:
"""
Args:
stage: "train", "valid" or "test"
outputs: model outputs for calculating loss
labels: labels for calculating loss
Returns:
loss
"""
raise NotImplementedError
@staticmethod
def load_weights(model, weights):
model_dict = model.state_dict()
unused_params = []
missed_params = list(model_dict.keys())
for k, v in weights.items():
if k in model_dict.keys():
model_dict[k] = v
missed_params.remove(k)
else:
unused_params.append(k)
if len(missed_params) > 0:
print(f"\033[31mSome weights of {type(model).__name__} were not "
f"initialized from the model checkpoint: {missed_params}\033[0m")
if len(unused_params) > 0:
print(f"\033[31mSome weights of the model checkpoint were not used: {unused_params}\033[0m")
model.load_state_dict(model_dict)
# Add 1 to step after each optimizer step
def optimizer_step(
self,
epoch: int,
batch_idx: int,
optimizer,
optimizer_idx: int = 0,
optimizer_closure=None,
on_tpu: bool = False,
using_native_amp: bool = False,
using_lbfgs: bool = False,
) -> None:
super().optimizer_step(
epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs
)
self.step += 1
def on_train_epoch_end(self):
self.epoch += 1
def training_step(self, batch, batch_idx):
inputs, labels = batch
outputs = self(**inputs)
loss = self.loss_func('train', outputs, labels)
return loss
def validation_step(self, batch, batch_idx):
inputs, labels = batch
outputs = self(**inputs)
return self.loss_func('valid', outputs, labels)
def test_step(self, batch, batch_idx):
inputs, labels = batch
outputs = self(**inputs)
return self.loss_func('test', outputs, labels)
def load_checkpoint(self, from_checkpoint, load_prev_scheduler):
state_dict = torch.load(from_checkpoint, map_location=self.device)
self.load_weights(self.model, state_dict["model"])
if load_prev_scheduler:
try:
self.step = state_dict["global_step"]
self.epoch = state_dict["epoch"]
self.best_value = state_dict["best_value"]
self.optimizer.load_state_dict(state_dict["optimizer"])
self.lr_scheduler.load_state_dict(state_dict["lr_scheduler"])
print(f"Previous training global step: {self.step}")
print(f"Previous training epoch: {self.epoch}")
print(f"Previous best value: {self.best_value}")
print(f"Previous lr_scheduler: {state_dict['lr_scheduler']}")
except Exception as e:
print(e)
raise KeyError("Wrong in loading previous scheduler, please set load_prev_scheduler=False")
def save_checkpoint(self, save_info: dict = None) -> None:
"""
Save model to save_path
Args:
save_info: Other info to save
"""
state_dict = {} if save_info is None else save_info
state_dict["model"] = self.model.state_dict()
if not self.save_weights_only:
state_dict["global_step"] = self.step
state_dict["epoch"] = self.epoch
state_dict["best_value"] = getattr(self, f"best_value", None)
state_dict["optimizer"] = self.optimizers().optimizer.state_dict()
state_dict["lr_scheduler"] = self.lr_schedulers().state_dict()
torch.save(state_dict, self.save_path)
def check_save_condition(self, now_value: float, mode: str, save_info: dict = None) -> None:
"""
Check whether to save model. If save_path is not None and now_value is the best, save model.
Args:
now_value: Current metric value
mode: "min" or "max", meaning whether the lower the better or the higher the better
save_info: Other info to save
"""
assert mode in ["min", "max"], "mode should be 'min' or 'max'"
if self.save_path is not None:
dir = os.path.dirname(self.save_path)
os.makedirs(dir, exist_ok=True)
if dist.get_rank() == 0:
# save the best checkpoint
best_value = getattr(self, f"best_value", None)
if best_value:
if mode == "min" and now_value < best_value or mode == "max" and now_value > best_value:
setattr(self, "best_value", now_value)
self.save_checkpoint(save_info)
else:
setattr(self, "best_value", now_value)
self.save_checkpoint(save_info)
def reset_metrics(self, stage) -> None:
"""
Reset metrics for given stage
Args:
stage: "train", "valid" or "test"
"""
for metric in self.metrics[stage].values():
metric.reset()
def get_log_dict(self, stage: str) -> dict:
"""
Get log dict for the stage
Args:
stage: "train", "valid" or "test"
Returns:
A dictionary of metrics for the stage. Keys are metric names and values are metric values
"""
return {name: metric.compute() for name, metric in self.metrics[stage].items()}
def log_info(self, info: dict) -> None:
"""
Record metrics during training and testing
Args:
info: dict of metrics
"""
if getattr(self, "logger", None) is not None:
info["learning_rate"] = self.lr_scheduler.get_last_lr()[0]
info["epoch"] = self.epoch
self.logger.log_metrics(info, step=self.step)
def init_optimizers(self):
# No decay for layer norm and bias
no_decay = ['LayerNorm.weight', 'bias']
if "weight_decay" in self.optimizer_kwargs:
weight_decay = self.optimizer_kwargs.pop("weight_decay")
else:
weight_decay = 0.01
optimizer_grouped_parameters = [
{'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': weight_decay},
{'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
'weight_decay': 0.0}
]
self.optimizer = torch.optim.AdamW(optimizer_grouped_parameters,
lr=self.lr_scheduler_kwargs['init_lr'],
**self.optimizer_kwargs)
self.lr_scheduler = Esm2LRScheduler(self.optimizer, **self.lr_scheduler_kwargs)
def configure_optimizers(self):
return {"optimizer": self.optimizer,
"lr_scheduler": {"scheduler": self.lr_scheduler,
"interval": "step",
"frequency": 1}
}
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