File size: 15,009 Bytes
52da96f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 |
import torch
import abc
import os
import copy
import pytorch_lightning as pl
from utils.lr_scheduler import *
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 checkpoint
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
if lr_scheduler_kwargs is None:
# Default lr_scheduler
self.lr_scheduler_kwargs = {
"class": "ConstantLRScheduler",
"init_lr": 0,
}
print("No lr_scheduler_kwargs provided. The default learning rate is 0.")
else:
self.lr_scheduler_kwargs = lr_scheduler_kwargs
if optimizer_kwargs is None:
# Default optimizer
self.optimizer_kwargs = {
"class": "AdamW",
"betas": (0.9, 0.98),
"weight_decay": 0.01,
}
print("No optimizer_kwargs provided. The default optimizer is AdamW.")
else:
self.optimizer_kwargs = optimizer_kwargs
self.init_optimizers()
self.save_path = save_path
self.save_weights_only = save_weights_only
# temp_step is used for accumulating gradients
self.temp_step = 0
self.step = 0
self.epoch = 0
self.load_prev_scheduler = load_prev_scheduler
self.from_checkpoint = from_checkpoint
if from_checkpoint:
self.load_checkpoint(from_checkpoint)
@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)
def optimizer_step(
self,
epoch: int,
batch_idx: int,
optimizer,
optimizer_closure=None,
) -> None:
super().optimizer_step(epoch, batch_idx, optimizer, optimizer_closure)
self.temp_step += 1
if self.temp_step == self.trainer.accumulate_grad_batches:
self.step += 1
self.temp_step = 0
# For pytorch-lightning 1.9.5
# 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.temp_step += 1
# if self.temp_step == self.trainer.accumulate_grad_batches:
# self.step += 1
# self.temp_step = 0
def on_train_epoch_end(self):
self.epoch += 1
def training_step(self, batch, batch_idx):
inputs, labels = batch
# optimizer = torch.optim.AdamW(self.model.parameters(), lr=5e-4, weight_decay=0.01, betas=(0.9, 0.98))
# for _ in range(1000):
# outputs = self(**inputs)
# loss = self.loss_func('train', outputs, labels)
# loss.backward()
# optimizer.step()
# optimizer.zero_grad()
#
# raise
outputs = self(**inputs)
loss = self.loss_func('train', outputs, labels)
self.log("loss", loss, prog_bar=True)
return loss
def validation_step(self, batch, batch_idx):
inputs, labels = batch
outputs = self(**inputs)
loss = self.loss_func('valid', outputs, labels)
self.valid_outputs.append(loss)
return loss
def test_step(self, batch, batch_idx):
inputs, labels = batch
outputs = self(**inputs)
loss = self.loss_func('test', outputs, labels)
self.test_outputs.append(loss)
return loss
def on_train_start(self) -> None:
# Load previous scheduler
if getattr(self, "prev_schechuler", None) is not None:
try:
self.step = self.prev_schechuler["global_step"]
self.epoch = self.prev_schechuler["epoch"]
self.best_value = self.prev_schechuler["best_value"]
self.lr_scheduler.load_state_dict(self.prev_schechuler["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: {self.prev_schechuler['lr_scheduler']}")
# Load optimizer state
if hasattr(self.trainer.strategy, "deepspeed_engine"):
# For DeepSpeed strategy
try:
self.trainer.strategy.deepspeed_engine.load_checkpoint(self.from_checkpoint)
except Exception as e:
print(e)
else:
# For DDP strategy
self.optimizer.load_state_dict(self.prev_schechuler["optimizer"])
except Exception as e:
print(e)
raise Exception("Error in loading previous scheduler. Please set load_prev_scheduler=False")
def on_validation_epoch_start(self) -> None:
setattr(self, "valid_outputs", [])
def on_test_epoch_start(self) -> None:
setattr(self, "test_outputs", [])
def load_checkpoint(self, from_checkpoint: str) -> None:
"""
Args:
from_checkpoint: Path to checkpoint.
"""
# If ``from_checkpoint`` is a directory, load the checkpoint in it
if os.path.isdir(from_checkpoint):
basename = os.path.basename(from_checkpoint)
from_checkpoint = os.path.join(from_checkpoint, f"{basename}.pt")
state_dict = torch.load(from_checkpoint, map_location=self.device)
self.load_weights(self.model, state_dict["model"])
if self.load_prev_scheduler:
state_dict.pop("model")
self.prev_schechuler = state_dict
def save_checkpoint(self, save_path: str, save_info: dict = None, save_weights_only: bool = True) -> None:
"""
Save model to save_path
Args:
save_path: Path to save model
save_info: Other info to save
save_weights_only: Whether only save model weights
"""
dir = os.path.dirname(save_path)
os.makedirs(dir, exist_ok=True)
state_dict = {} if save_info is None else save_info
state_dict["model"] = self.model.state_dict()
# Convert model weights to fp32
for k, v in state_dict["model"].items():
state_dict["model"][k] = v.float()
if not 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["lr_scheduler"] = self.lr_schedulers().state_dict()
# If not using DeepSpeed, save optimizer state
if not hasattr(self.trainer.strategy, "deepspeed_engine"):
state_dict["optimizer"] = self.optimizers().optimizer.state_dict()
torch.save(state_dict, 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:
# In case there are variables to be included in the save path
save_path = eval(f"f'{self.save_path}'")
dir = os.path.dirname(save_path)
os.makedirs(dir, exist_ok=True)
# Check whether to save model
best_value = getattr(self, f"best_value", None)
if best_value is not None:
if mode == "min" and now_value >= best_value or mode == "max" and now_value <= best_value:
return
setattr(self, "best_value", now_value)
# For DeepSpeed strategy
if hasattr(self.trainer.strategy, "deepspeed_engine"):
if not self.save_weights_only:
self.trainer.strategy.deepspeed_engine.save_checkpoint(save_path, tag="deepspeed_ckpt")
# Save a complete checkpoint
if dist.get_rank() == 0:
basename = os.path.basename(save_path)
ckpt_path = os.path.join(save_path, f"{basename}.pt")
self.save_checkpoint(ckpt_path, save_info, self.save_weights_only)
# For normal situation
else:
if dist.get_rank() == 0:
self.save_checkpoint(save_path, save_info, self.save_weights_only)
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 and dist.get_rank() == 0:
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):
copy_optimizer_kwargs = copy.deepcopy(self.optimizer_kwargs)
# No decay for layer norm and bias
no_decay = ['LayerNorm.weight', 'bias']
weight_decay = copy_optimizer_kwargs.pop("weight_decay")
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}
]
optimizer_cls = eval(f"torch.optim.{copy_optimizer_kwargs.pop('class')}")
self.optimizer = optimizer_cls(optimizer_grouped_parameters,
lr=self.lr_scheduler_kwargs['init_lr'],
**copy_optimizer_kwargs)
tmp_kwargs = copy.deepcopy(self.lr_scheduler_kwargs)
lr_scheduler = tmp_kwargs.pop("class")
self.lr_scheduler = eval(lr_scheduler)(self.optimizer, **tmp_kwargs)
def configure_optimizers(self):
return {"optimizer": self.optimizer,
"lr_scheduler": {"scheduler": self.lr_scheduler,
"interval": "step",
"frequency": 1}
}
|