File size: 18,399 Bytes
02e480f da2208c 02e480f da2208c 02e480f da2208c 02e480f da2208c 02e480f da2208c 02e480f da2208c 02e480f da2208c 02e480f da2208c 02e480f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 |
# Deep learning
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from torch.utils.data import DataLoader
from torch.nn.parallel import DistributedDataParallel as DDP
from fast_transformers.masking import LengthMask
# Standard library
from tqdm import tqdm
import pandas as pd
import numpy as np
import random
import os
class Trainer:
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
save_checkpoint_path: str,
load_checkpoint_path: str,
config,
) -> None:
self.local_rank = int(os.environ["LOCAL_RANK"])
self.global_rank = int(os.environ["RANK"])
self.model = model.to(self.local_rank)
self.train_data = train_data
self.optimizer = optimizer
self.save_every = save_every
self.epochs_run = 0
self.last_batch_idx = -1
self.save_checkpoint_path = save_checkpoint_path
self.config = config
if os.path.exists(load_checkpoint_path):
print(f"Loading checkpoint at {load_checkpoint_path}...")
self._load_checkpoint(load_checkpoint_path)
self.model = DDP(self.model, device_ids=[self.local_rank])
def _load_checkpoint(self, checkpoint_path):
opt_dict = None
loc = f"cuda:{self.local_rank}"
ckpt_dict = torch.load(checkpoint_path, map_location=loc)
if os.path.exists(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt')):
opt_dict = torch.load(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'), map_location=loc)
self.model.load_state_dict(ckpt_dict["MODEL_STATE"])
if opt_dict is not None:
self.optimizer.load_state_dict(opt_dict["OPTIMIZER_STATE"])
print('Optimizer states restored!')
self.last_batch_idx = ckpt_dict["last_batch_idx"] if 'last_batch_idx' in ckpt_dict else -1
self.epochs_run = ckpt_dict["EPOCHS_RUN"] + 1 if self.last_batch_idx == -1 else ckpt_dict["EPOCHS_RUN"]
# load RNG states each time the model and states are loaded from checkpoint
if 'rng' in ckpt_dict:
rng = ckpt_dict['rng']
for key, value in rng.items():
if key =='torch_state':
torch.set_rng_state(value.cpu())
elif key =='cuda_state':
torch.cuda.set_rng_state(value.cpu())
elif key =='numpy_state':
np.random.set_state(value)
elif key =='python_state':
random.setstate(value)
else:
print('unrecognized state')
print(f"Resuming training from checkpoint at Epoch {self.epochs_run}.")
def _save_checkpoint(self, epoch, config, last_idx):
# save RNG states each time the model and states are saved
out_dict = dict()
out_dict['torch_state'] = torch.get_rng_state()
out_dict['cuda_state'] = torch.cuda.get_rng_state()
if np:
out_dict['numpy_state'] = np.random.get_state()
if random:
out_dict['python_state'] = random.getstate()
# model states
ckpt_dict = {
"MODEL_STATE": self.model.module.state_dict(),
"EPOCHS_RUN": epoch,
"hparams": vars(config),
"last_batch_idx": last_idx,
"rng": out_dict
}
# optimizer states
opt_dict = {
"OPTIMIZER_STATE": self.optimizer.state_dict(),
}
if last_idx == -1:
filename = f'{str(self.model.module)}_{epoch}.pt'
else:
filename = f'{str(self.model.module)}_{last_idx}_{epoch}.pt'
torch.save(ckpt_dict, os.path.join(self.save_checkpoint_path, filename))
torch.save(opt_dict, os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'))
print(f"Epoch {epoch} | Training checkpoint saved at {os.path.join(self.save_checkpoint_path, filename)}.")
def train(self, max_epochs: int):
for epoch in range(self.epochs_run, max_epochs):
self._run_epoch(epoch)
if self.local_rank == 0:
self._save_checkpoint(epoch, self.config, last_idx=-1)
def _run_epoch(self, epoch):
print(f"[GPU{self.global_rank}] Epoch {epoch} | Batchsize: {self.config.n_batch} | Steps: {len(self.train_data)} | Last batch: {self.last_batch_idx}")
self.train_data.sampler.set_epoch(epoch)
loss_list = pd.Series()
for idx, data in enumerate(tqdm(self.train_data)):
# skip batches
if idx <= self.last_batch_idx:
continue
# run batch
bucket_idx_masked = data[0]
bucket_targets = data[1]
bucket_idx_not_masked = data[2]
loss = self._run_batch(bucket_idx_masked, bucket_targets, bucket_idx_not_masked)
torch.cuda.empty_cache()
# track loss
if self.local_rank == 0:
loss_list = pd.concat([loss_list, pd.Series([loss])], axis=0)
# checkpoint
if self.local_rank == 0 and idx % self.save_every == 0 and idx != 0:
self._save_checkpoint(epoch, self.config, idx)
# WARN: due to job limit time - save loss for each iter
loss_list.to_csv(os.path.join(self.config.save_checkpoint_path, f'training_loss_{idx}_epoch{epoch}.csv'), index=False)
loss_list = pd.Series()
self.last_batch_idx = -1
if self.local_rank == 0:
loss_list.to_csv(os.path.join(self.config.save_checkpoint_path, f'training_loss_epoch{epoch}.csv'), index=False)
def _run_batch(self, bucket_idx_masked, bucket_targets, bucket_idx_not_masked):
raise NotImplementedError
class TrainerEncoderDecoder(Trainer):
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
save_checkpoint_path: str,
load_checkpoint_path: str,
config,
) -> None:
super().__init__(model, train_data, optimizer, save_every, save_checkpoint_path, load_checkpoint_path, config)
self.criterionC = nn.CrossEntropyLoss(ignore_index=-100)
self.criterionR = nn.MSELoss()
self.optimE = self.optimizer[0]
self.optimD = self.optimizer[1]
self.ngpus_per_node = torch.cuda.device_count()
self.total_batches = len(self.train_data)
self.batch_thresh = int(self.total_batches - (self.total_batches * 0.05 * self.ngpus_per_node))
print('batch_thresh:', self.batch_thresh)
def _load_checkpoint(self, checkpoint_path):
opt_dict = None
loc = f"cuda:{self.local_rank}"
ckpt_dict = torch.load(checkpoint_path, map_location=loc)
if os.path.exists(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt')):
opt_dict = torch.load(os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'), map_location=loc)
self.model.load_state_dict(ckpt_dict["MODEL_STATE"])
if opt_dict is not None:
self.optimizer[0].load_state_dict(opt_dict["OPTIMIZER_STATE_ENCODER"])
self.optimizer[1].load_state_dict(opt_dict["OPTIMIZER_STATE_DECODER"])
print('Optimizer states restored!')
self.last_batch_idx = ckpt_dict["last_batch_idx"] if 'last_batch_idx' in ckpt_dict else -1
self.epochs_run = ckpt_dict["EPOCHS_RUN"] + 1 if self.last_batch_idx == -1 else ckpt_dict["EPOCHS_RUN"]
# load RNG states each time the model and states are loaded from checkpoint
if 'rng' in ckpt_dict:
rng = ckpt_dict['rng']
for key, value in rng.items():
if key =='torch_state':
torch.set_rng_state(value.cpu())
elif key =='cuda_state':
torch.cuda.set_rng_state(value.cpu())
elif key =='numpy_state':
np.random.set_state(value)
elif key =='python_state':
random.setstate(value)
else:
print('unrecognized state')
print(f"Resuming training from checkpoint at Epoch {self.epochs_run}.")
def _save_checkpoint(self, epoch, config, last_idx):
# save RNG states each time the model and states are saved
out_dict = dict()
out_dict['torch_state'] = torch.get_rng_state()
out_dict['cuda_state'] = torch.cuda.get_rng_state()
if np:
out_dict['numpy_state'] = np.random.get_state()
if random:
out_dict['python_state'] = random.getstate()
# model states
ckpt_dict = {
"MODEL_STATE": self.model.module.state_dict(),
"EPOCHS_RUN": epoch,
"hparams": vars(config),
"last_batch_idx": last_idx,
"rng": out_dict
}
# optimizer states
opt_dict = {
"OPTIMIZER_STATE_ENCODER": self.optimizer[0].state_dict(),
"OPTIMIZER_STATE_DECODER": self.optimizer[1].state_dict(),
}
if last_idx == -1:
filename = f'{str(self.model.module)}_{epoch}.pt'
else:
filename = f'{str(self.model.module)}_{last_idx}_{epoch}.pt'
torch.save(ckpt_dict, os.path.join(self.save_checkpoint_path, filename))
torch.save(opt_dict, os.path.join(self.save_checkpoint_path, 'OPTIMIZER_STATES.pt'))
print(f"Epoch {epoch} | Training checkpoint saved at {os.path.join(self.save_checkpoint_path, filename)}.")
def _run_epoch(self, epoch):
print(f"[GPU{self.global_rank}] Epoch {epoch} | Batchsize: {self.config.n_batch} | Steps: {len(self.train_data)}")
self.train_data.sampler.set_epoch(epoch)
loss_list = pd.DataFrame()
for idx, data in enumerate(tqdm(self.train_data)):
bucket_idx_masked = data[0]
bucket_targets = data[1]
bucket_idx_not_masked = data[2]
lossE, lossD = self._run_batch(idx, bucket_idx_masked, bucket_targets, bucket_idx_not_masked)
torch.cuda.empty_cache()
if self.local_rank == 0:
df = pd.DataFrame({
'lossE': [lossE.cpu().item()],
'lossD': [lossD.cpu().item()],
})
loss_list = pd.concat([loss_list, df], axis=0)
if self.local_rank == 0:
loss_list.to_csv(os.path.join(self.config.save_checkpoint_path, f'training_loss_epoch{epoch}.csv'), index=False)
def custom(self, module):
def custom_forward(*inputs):
inputs = module(inputs[0])
return inputs
return custom_forward
def _run_batch(self, batch_idx, bucket_idx_masked, bucket_targets, bucket_idx_not_masked):
self.optimE.zero_grad(set_to_none=True)
self.optimD.zero_grad(set_to_none=True)
can_train_encoder = (batch_idx + 1) <= self.batch_thresh
can_train_decoder = (batch_idx + 1) > self.batch_thresh
padding_idx = 2
errorE = torch.zeros(1).to(self.local_rank)
errorD = torch.zeros(1).to(self.local_rank)
errorE_tmp = .0
errorD_tmp = .0
for chunk in range(len(bucket_idx_masked)):
idx_masked = bucket_idx_masked[chunk].to(self.local_rank)
targets = bucket_targets[chunk].to(self.local_rank)
idx_not_masked = bucket_idx_not_masked[chunk]
idx_not_masked = list(map(lambda x: F.pad(x, pad=(0, self.config.max_len - x.shape[0]), value=2).unsqueeze(0), idx_not_masked))
idx_not_masked = torch.cat(idx_not_masked, dim=0).to(self.local_rank)
mask = (idx_masked != padding_idx)
###########
# Encoder #
###########
if can_train_encoder:
for param in self.model.module.encoder.parameters():
param.requires_grad = True
for param in self.model.module.decoder.parameters():
param.requires_grad = False
# encoder forward
x = self.model.module.encoder.tok_emb(idx_masked)
x = self.model.module.encoder.drop(x)
x = checkpoint.checkpoint(self.custom(self.model.module.encoder.blocks), x)
logits = self.model.module.encoder.lang_model(x)
# loss function
logits = logits.view(-1, logits.size(-1))
targets = targets.view(-1)
errorE_tmp = self.criterionC(logits, targets) / len(bucket_idx_masked)
if chunk < len(bucket_idx_masked)-1:
errorE_tmp.backward()
errorE += errorE_tmp.detach()
else:
errorE += errorE_tmp
###########
# Decoder #
###########
if can_train_decoder:
for param in self.model.module.encoder.parameters():
param.requires_grad = False
for param in self.model.module.decoder.parameters():
param.requires_grad = True
self.model.module.encoder.eval()
# encoder forward
with torch.no_grad():
true_set, true_cte = self.model.module.encoder(idx_masked, mask=mask, inference=True)
# add padding
input_mask_expanded = mask.unsqueeze(-1).expand(true_cte.size()).float()
mask_embeddings = (true_cte * input_mask_expanded)
true_cte = F.pad(mask_embeddings, pad=(0, 0, 0, self.config.max_len - mask_embeddings.shape[1]), value=0)
true_cte = true_cte.view(-1, self.config.max_len*self.config.n_embd)
# decoder forward
pred_set, pred_ids = self.model.module.decoder(true_cte)
# losses
pred_ids = pred_ids.view(-1, pred_ids.size(-1))
true_ids = idx_not_masked.view(-1)
error_ids = self.criterionC(pred_ids, true_ids) / len(bucket_idx_masked)
error_set = self.criterionR(pred_set, true_set) / len(bucket_idx_masked)
errorD_tmp = error_ids + error_set
if chunk < len(bucket_idx_masked)-1:
errorD_tmp.backward()
errorD += errorD_tmp.detach()
else:
errorD += errorD_tmp
if can_train_decoder:
errorD.backward()
self.optimD.step()
elif can_train_encoder:
errorE.backward()
self.optimE.step()
if self.local_rank == 0:
print(f'LossE: {errorE.item()} | LossD: {errorD.item()}')
return errorE, errorD
class TrainerDirectDecoder(Trainer):
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
save_every: int,
save_checkpoint_path: str,
load_checkpoint_path: str,
config,
) -> None:
super().__init__(model, train_data, optimizer, save_every, save_checkpoint_path, load_checkpoint_path, config)
self.criterionC = nn.CrossEntropyLoss(ignore_index=-100)
self.criterionR = nn.MSELoss()
def custom(self, module):
def custom_forward(*inputs):
inputs = module(inputs[0], length_mask=inputs[1])
return inputs
return custom_forward
def _run_batch(self, bucket_idx_masked, bucket_targets, bucket_idx_not_masked):
padding_idx = 2
error = torch.zeros(1).to(self.local_rank)
error_tmp = .0
self.optimizer.zero_grad(set_to_none=True)
for chunk in range(len(bucket_idx_masked)):
idx_masked = bucket_idx_masked[chunk].to(self.local_rank)
targets = bucket_targets[chunk].to(self.local_rank)
idx_not_masked = bucket_idx_not_masked[chunk]
idx_not_masked = list(map(lambda x: F.pad(x, pad=(0, self.config.max_len - x.shape[0]), value=2).unsqueeze(0), idx_not_masked))
idx_not_masked = torch.cat(idx_not_masked, dim=0).to(self.local_rank)
mask = (idx_masked != padding_idx)
# encoder forward
x = self.model.module.encoder.tok_emb(idx_masked)
x = self.model.module.encoder.drop(x)
x = checkpoint.checkpoint(self.custom(self.model.module.encoder.blocks), x, LengthMask(mask.sum(-1), max_len=idx_masked.shape[1]))
# mean pooling
input_masked_expanded = mask.unsqueeze(-1).expand(x.size()).float()
sum_embeddings = torch.sum(x*input_masked_expanded, 1)
sum_mask = torch.clamp(input_masked_expanded.sum(1), min=1e-9)
true_set = sum_embeddings/sum_mask
true_cte = x
del x
torch.cuda.empty_cache()
# add padding
input_mask_expanded = mask.unsqueeze(-1).expand(true_cte.size()).float()
mask_embeddings = (true_cte * input_mask_expanded)
true_cte = F.pad(mask_embeddings, pad=(0, 0, 0, self.config.max_len - mask_embeddings.shape[1]), value=0)
true_cte = true_cte.view(-1, self.config.max_len*self.config.n_embd)
# decoder forward
pred_set, pred_ids = self.model.module.decoder(true_cte)
# losses
pred_ids = pred_ids.view(-1, pred_ids.size(-1))
true_ids = idx_not_masked.view(-1)
error_ids = self.criterionC(pred_ids, true_ids) / len(bucket_idx_masked)
error_set = self.criterionR(pred_set, true_set) / len(bucket_idx_masked)
error_tmp = error_ids + error_set
if chunk < len(bucket_idx_masked)-1:
error_tmp.backward()
error += error_tmp.detach()
else:
error += error_tmp
torch.cuda.empty_cache()
error.backward()
self.optimizer.step()
if self.local_rank == 0:
print(f'Loss: {error.item()}')
return error.item() |