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import time
import os, random
import torch
import math, pickle
from tqdm import tqdm
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
import torch.nn as nn
import torch.distributed as dist
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from torch.utils.data.distributed import DistributedSampler
import logging
from data import gigaspeech
from models import voicecraft
from .trainer_utils import DistributedDynamicBatchSampler, StatefulDistributedSampler, AverageMeter, print_model_info
from .optim import ScaledAdam, Eden
class Trainer:
def __init__(self, args, world_size, rank):
self.start_time = time.time()
self.args = args
self.world_size, self.rank = world_size, rank
self.device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
if self.rank == 0:
self.writer = SummaryWriter(args.exp_dir)
self.seed_everything(seed=self.args.seed)
self.meters = self._setup_meters()
self.progress, self.total_progress = self._setup_progress()
self.model, self.trainables, self.optim_states, self.scheduler_states = self._setup_models()
self.train_dataset_length, self.train_sampler, self.train_loader, self.valid_loader = self._setup_dataloader()
if self.args.num_steps != None:
self.total_step = self.args.num_steps
self.args.num_epochs = math.ceil(self.total_step / math.floor(self.train_dataset_length / self.args.batch_size)) if not self.args.dynamic_batching else None
else:
self.total_step = int(math.floor(self.train_dataset_length / self.args.batch_size))*self.args.num_epochs
self.optimizer, self.scheduler = self._setup_optimizer()
self.scaler = torch.cuda.amp.GradScaler()
self.model = torch.nn.parallel.DistributedDataParallel(self.model, device_ids=[self.rank], find_unused_parameters=False)
if self.rank == 0:
self.early_stop_accu_steps = 0
if self.args.dynamic_batching:
logging.info(f"max number of tokens per GPU in a training batch: {self.args.max_num_tokens}, max number of tokens per GPU in a inference batch: {self.args.val_max_num_tokens}")
else:
logging.info(f"batch size (summed over all GPUs): {self.args.batch_size}")
def train(self):
flag = True
skip_flag = False
data_start_time = time.time()
while flag:
self.train_sampler.set_epoch(self.progress['epoch'])
for i, batch in enumerate(self.train_loader):
data_end_time = time.time()
self.model.train()
if self.progress['step'] > self.total_step:
flag = False
self.validate_and_save()
if self.rank == 0:
self.writer.close()
break
if isinstance(self.scheduler, Eden):
self.scheduler.step_epoch(self.progress['step']//self.args.pseudo_epoch_size + 1)
if self.args.optimizer_name == "ScaledAdam":
cur_lr = self.scheduler.get_last_lr()[0]
else:
lrs = [param_group['lr'] for param_group in self.optimizer.param_groups]
assert lrs[0] == lrs[1]
cur_lr = lrs[0]
if self.rank == 0 and self.progress['step'] % self.args.tb_write_every_n_steps == 0:
self.writer.add_scalar("train/lr", cur_lr, self.progress['step'])
self.wandb.log({"train/lr": cur_lr}, step=self.progress['step'])
all_inds = list(range(len(batch['y'])))
sum_losses = 0
sum_top10acc = 0
sum_ntoken = 0
sum_top10acc_cbi = [0 for _ in range(self.args.n_codebooks)]
for j in range(self.args.gradient_accumulation_steps):
cur_ind = all_inds[j::self.args.gradient_accumulation_steps]
cur_batch = {key: batch[key][cur_ind] for key in batch}
with torch.cuda.amp.autocast(dtype=torch.float16 if self.args.precision=="float16" else torch.float32):
out = self.model(cur_batch)
record_loss = out['loss'].detach().to(self.rank)
top10acc = out['top10acc'].to(self.rank)
effective_ntoken = out['effective_ntoken'].to(self.rank)
is_nan = torch.tensor(int(torch.isnan(record_loss).any()), dtype=torch.float32, device=self.rank)
dist.all_reduce(record_loss, op=dist.ReduceOp.SUM)
dist.all_reduce(top10acc, op=dist.ReduceOp.SUM)
dist.all_reduce(effective_ntoken, op=dist.ReduceOp.SUM)
dist.all_reduce(is_nan, op=dist.ReduceOp.SUM)
# check if loss is nan
if is_nan.item() > 0:
logging.info(f"loss at step {self.progress['step']} is nan, therefore skip this batch")
skip_flag = True
continue
sum_losses += record_loss.item()
sum_top10acc += top10acc.item()
sum_ntoken += effective_ntoken.item()
if 'top10acc_by_codebook' in out:
for cb in range(self.args.n_codebooks):
top10acc_cbi = out['top10acc_by_codebook'][cb]
dist.all_reduce(top10acc_cbi, op=dist.ReduceOp.SUM)
sum_top10acc_cbi[cb] += top10acc_cbi.item()
if self.rank == 0:
average_loss = sum_losses / sum_ntoken
average_top10acc = sum_top10acc / sum_ntoken
self.meters['train_loss'].update(average_loss, batch['x'].shape[0]*self.world_size)
self.meters['train_top10acc'].update(average_top10acc, batch['x'].shape[0]*self.world_size)
self.meters['train_top10acc'].update(average_top10acc, batch['x'].shape[0]*self.world_size)
average_top10acc_cbi = [sum_top10acc_cbi[cb] / sum_ntoken * self.args.n_codebooks for cb in range(self.args.n_codebooks)]
for cb in range(self.args.n_codebooks):
self.meters[f'train_top10acc_cb{cb+1}'].update(average_top10acc_cbi[cb], batch['x'].shape[0]*self.world_size)
if self.progress['step'] % self.args.tb_write_every_n_steps == 0:
self.writer.add_scalar('train/loss', average_loss, self.progress['step'])
self.writer.add_scalar('train/top10acc', average_top10acc, self.progress['step'])
self.writer.add_scalar("train/ntokens", sum_ntoken, self.progress['step'])
for cb in range(self.args.n_codebooks):
self.writer.add_scalar(f'train/top10acc_cb{cb+1}', average_top10acc_cbi[cb], self.progress['step'])
if self.args.optimizer_name == "ScaledAdam":
self.scaler.scale(out['loss']).backward()
else:
self.scaler.scale(out['loss']/out['effective_ntoken']).backward()
if skip_flag:
self.optimizer.zero_grad()
skip_flag = False
continue
if self.args.optimizer_name != "ScaledAdam":
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.gradient_clip_val)
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad()
if self.args.optimizer_name == "ScaledAdam":
self.scheduler.step_batch(self.progress['step'])
else:
self.scheduler.step()
if self.rank == 0:
self.meters['data_time'].update(data_end_time - data_start_time)
self.meters['train_time'].update(time.time() - data_end_time)
if self.progress['step'] % self.args.tb_write_every_n_steps == 0:
self.writer.add_scalar("train/data_time", data_end_time - data_start_time, self.progress['step'])
self.writer.add_scalar("train/train_time", time.time() - data_end_time, self.progress['step'])
# logging
if self.progress['step'] % self.args.print_every_n_steps == 0:
log_out = {}
log_out['cur_epoch'] = f"{self.progress['epoch']}/{self.args.num_epochs}" if self.args.num_epochs is not None else f"{self.progress['epoch']}"
log_out['cur_step'] = f"{int(self.progress['cur_step']+1)}"
log_out['total_step'] = f"{self.progress['step']}/{self.args.num_steps}"
log_out['lr'] = f"{cur_lr:.7f}"
log_out['ntokens'] = f"{sum_ntoken}"
for key in self.meters:
if self.meters[key].val != 0 or self.meters[key].avg != 0:
log_out[key] = f"{self.meters[key].val:.4f} ({self.meters[key].avg:.4f})" if isinstance(self.meters[key].val, float) else f"{self.meters[key].val}"
logging.info(log_out)
if np.isnan(self.meters['train_loss'].avg):
logging.warning("training diverged...")
raise RuntimeError("training diverged...")
# validation and save models
if self.progress['step'] % self.args.val_every_n_steps == 0:
dist.barrier()
self.validate_and_save()
self.progress['step'] += 1
self.progress['cur_step'] += 1
data_start_time = time.time()
self.progress['epoch'] += 1
self.progress['cur_step'] = 0 # reset cur_step to be 0
dist.destroy_process_group()
def validate_and_save(self):
self.model.eval()
score = self.validate(self.valid_loader)
if self.rank == 0:
if self.args.early_stop_threshold > 0:
if self.progress['best_score'] - score < self.args.early_stop_threshold:
self.early_stop_accu_steps += self.args.val_every_n_steps
if self.early_stop_accu_steps >= self.args.early_stop_step-1:
logging.info(f"early stop based on self.args.early_stop_threshold: {self.args.early_stop_threshold}, and self.args.early_stop_step: {self.args.early_stop_step}")
logging.info(f"best validation score at step: {self.progress['best_step']}, and the score is {self.progress['best_score']:.4f}")
dist.destroy_process_group()
raise RuntimeError("early stop")
else:
self.early_stop_accu_steps = 0
if (score < self.progress['best_score']):
self.progress['best_step'] = self.progress['step']
self.progress['best_score'] = score
save_path = os.path.join(self.args.exp_dir,"best_bundle.pth")
torch.save(
{
"model": self.model.module.state_dict(),
"optimizer": self.optimizer.state_dict(),
"scheduler": self.scheduler.state_dict(),
"config": self.args,
"phn2num": self.train_loader.dataset.phn2num
},save_path
)
logging.info(f"save *best* models at {save_path} at global step {self.progress['step']}")
self._save_progress()
save_path = os.path.join(self.args.exp_dir,"bundle.pth")
torch.save(
{
"model": self.model.module.state_dict(),
"optimizer": self.optimizer.state_dict(),
"scheduler": self.scheduler.state_dict(),
"config": self.args,
"phn2num": self.train_loader.dataset.phn2num
},save_path
)
logging.info(f"save models, indices, acc and other statistics at {save_path} and {self.args.exp_dir}/progress.pkl at global step {self.progress['step']}")
dist.barrier()
def validate(self, valid_loader=None, hide_progress=True):
if valid_loader == None:
valid_loader = self.valid_loader
self.model.eval()
start_val_time = time.time()
sum_losses = 0
sum_top10acc = 0
sum_ntoken = 0
sum_top10acc_cbi = [0 for _ in range(self.args.n_codebooks)]
with torch.no_grad():
for i, batch in enumerate(tqdm(valid_loader, disable=hide_progress)):
out = self.model(batch)
sum_losses += out['loss']
sum_top10acc += out['top10acc']
sum_ntoken += out['effective_ntoken']
if 'top10acc_by_codebook' in out:
for cb in range(self.args.n_codebooks):
sum_top10acc_cbi[cb] += out['top10acc_by_codebook'][cb]
dist.all_reduce(sum_losses, op=dist.ReduceOp.SUM)
dist.all_reduce(sum_top10acc, op=dist.ReduceOp.SUM)
dist.all_reduce(sum_ntoken, op=dist.ReduceOp.SUM)
if 'top10acc_by_codebook' in out:
for cb in range(self.args.n_codebooks):
dist.all_reduce(sum_top10acc_cbi[cb], op=dist.ReduceOp.SUM)
if self.rank == 0:
val_loss = sum_losses / sum_ntoken
val_top10acc = sum_top10acc / sum_ntoken
# logging
self.meters['val_loss'].update(val_loss)
logging.info(f"val loss: {val_loss:.5f}")
self.writer.add_scalar("val/loss", val_loss, self.progress['step'])
self.meters['val_top10acc'].update(val_top10acc)
logging.info(f"val top10acc: {val_top10acc:.5f}")
self.writer.add_scalar("val/top10acc", val_top10acc, self.progress['step'])
for cb in range(self.args.n_codebooks):
average_top10acc_cbi = sum_top10acc_cbi[cb] / sum_ntoken * self.args.n_codebooks
self.meters[f'val_top10acc_cb{cb+1}'].update(average_top10acc_cbi)
self.writer.add_scalar(f'val/top10acc_cb{cb+1}', average_top10acc_cbi, self.progress['step'])
logging.info(f"validation takes: {time.time() - start_val_time:.2f}s")
logging.info(f"Step [{self.progress['step']}/{self.total_step}]\t Time elapsed {(time.time() - self.start_time)/3600.:.2f}h, Val Loss: {val_loss:.4f}, Val Top10Acc: {val_top10acc:.4f}")
return val_loss.item()
else:
return None
def _setup_meters(self):
meters = {}
meter_names = ['train_loss', 'val_loss', 'train_top10acc', 'val_top10acc', 'data_time', 'train_time']
meter_names += ['train_dur_loss', 'train_dur_acc', 'val_dur_loss', 'val_dur_acc']
meter_names += [f'train_top10acc_cb{cb+1}' for cb in range(self.args.n_codebooks)]
meter_names += [f'val_top10acc_cb{cb+1}' for cb in range(self.args.n_codebooks)]
for name in meter_names:
meters[name] = AverageMeter()
return meters
def _setup_progress(self):
progress = {}
progress['best_step'] = 1
progress['best_score'] = np.inf # this records loss value
progress['step'] = 1
progress['epoch'] = 1
progress['cur_step'] = 0 # step in the current epoch, for resuming the sampler
total_progress = []
# if self.args.resume or self.args.validate:
if self.args.resume:
progress_pkl = "%s/progress.pkl" % self.args.exp_dir
with open(progress_pkl, "rb") as f:
total_progress = pickle.load(f)
progress['best_step'], progress['best_score'], progress['step'], progress['epoch'], progress['cur_step'], _ = total_progress[-1]
if self.rank == 0:
logging.info("\nResume training from:")
logging.info(" epoch = %s" % progress['epoch'])
logging.info(" cur_step = %s" % progress['cur_step'])
logging.info(" step = %s" % progress['step'])
logging.info(" best_step = %s" % progress['best_step'])
logging.info(" best_score = %s" % progress['best_score'])
return progress, total_progress
def _save_progress(self):
self.total_progress.append([self.progress['best_step'], self.progress['best_score'], int(self.progress['step']+1), self.progress['epoch'], int(self.progress['cur_step']+1), time.time() - self.start_time])
with open("%s/progress.pkl" % self.args.exp_dir, "wb") as f:
pickle.dump(self.total_progress, f)
def _setup_dataloader(self):
assert self.args.dataset == 'gigaspeech', "only gigaspeech is supported for now"
train_dataset, val_dataset = gigaspeech.dataset(self.args, 'train'), gigaspeech.dataset(self.args, 'validation')
if self.args.dynamic_batching:
train_sampler = DistributedDynamicBatchSampler(train_dataset, self.args, num_replicas=self.world_size, rank=self.rank, shuffle=True, seed=self.args.seed, drop_last=True, lengths_list=train_dataset.lengths_list, verbose=True, epoch=0)
valid_sampler = DistributedDynamicBatchSampler(val_dataset, self.args, num_replicas=self.world_size, rank=self.rank, shuffle=True, seed=self.args.seed, drop_last=True, lengths_list=val_dataset.lengths_list, verbose=True, epoch=0)
else:
train_sampler = StatefulDistributedSampler(train_dataset, self.args.batch_size//self.world_size, num_replicas=self.world_size, rank=self.rank, shuffle=True, seed=self.args.seed, drop_last=True)
valid_sampler = DistributedSampler(val_dataset, num_replicas=self.world_size, rank=self.rank, shuffle=False, seed=self.args.seed, drop_last=False)
if self.progress['step'] > 1:
train_sampler.set_epoch_resume(self.progress['epoch'], self.progress['cur_step'])
if self.args.dynamic_batching:
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_sampler=train_sampler,
num_workers=self.args.num_workers//self.world_size,
collate_fn=train_dataset.collate, persistent_workers=True
)
valid_loader = torch.utils.data.DataLoader(val_dataset,
batch_sampler=valid_sampler,
num_workers=self.args.num_workers//self.world_size,
collate_fn=val_dataset.collate, persistent_workers=True
)
else:
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=self.args.batch_size//self.world_size, sampler=train_sampler, num_workers=self.args.num_workers//self.world_size,
collate_fn=train_dataset.collate, persistent_workers=True
)
valid_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=self.args.batch_size//self.world_size, sampler=valid_sampler,
num_workers=self.args.num_workers//self.world_size,
collate_fn=val_dataset.collate, persistent_workers=True
)
return len(train_dataset), train_sampler, train_loader, valid_loader
def _setup_models(self):
model = voicecraft.VoiceCraft(self.args)
if self.rank == 0:
logging.info(model)
logging.info("model parameters")
print_model_info(model)
if self.progress['step'] > 1:
bundle = torch.load(os.path.join(self.args.exp_dir, "bundle.pth"), map_location="cpu")
model.load_state_dict(bundle['model'])
optim_states = bundle['optimizer']
scheduler_states = bundle['scheduler']
if self.rank == 0:
logging.info("loaded parameters and data indices from epoch %d, global step %d" % (self.progress['epoch'], self.progress['step']))
del bundle['model']
else:
optim_states = None
scheduler_states = None
if self.args.load_model_from != None and self.progress['step'] <= 1:
sd = torch.load(self.args.load_model_from, map_location="cpu")['model']
model.load_state_dict(sd)
del sd
if self.args.optimizer_name == "ScaledAdam":
trainables = [p for p in model.parameters() if p.requires_grad]
else:
no_decay = [".bias", ".audio_embeddings.weight", ".text_embeddings.weight", ".norm.weight", ".norm1.weight", ".norm2.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad],
"weight_decay": 0.0,
},
]
if len(optimizer_grouped_parameters[1]['params']) == 0:
logging.info("there is no embedding weights, bias, and layernorm parameters in the model, which should be True, check model parameter names")
trainables = optimizer_grouped_parameters[0]
else:
trainables = optimizer_grouped_parameters
model.to(self.device)
return model, trainables, optim_states, scheduler_states
def _setup_optimizer(self):
if self.args.optimizer_name == "ScaledAdam":
parameters_names = []
parameters_names.append([n for n,p in self.model.named_parameters() if p.requires_grad])
optimizer = ScaledAdam(
self.trainables,
lr=self.args.lr,
betas=(0.9, 0.95),
clipping_scale=2.0,
parameters_names=parameters_names,
show_dominant_parameters=False,
clipping_update_period=self.args.clipping_update_period,
)
scheduler = Eden(optimizer, self.args.reduce_lr_start_step, self.args.reduce_lr_start_epoch, warmup_batches=self.total_step * self.args.warmup_fraction)
else:
optimizer = AdamW(self.trainables, lr=self.args.lr)
warmup_steps = self.total_step * self.args.warmup_fraction
def lr_lambda(current_step: int):
if current_step < warmup_steps:
return float(current_step) / float(max(1, warmup_steps))
return max(
0.0, float(self.total_step - current_step) / float(max(1, self.total_step - warmup_steps))
)
scheduler = LambdaLR(optimizer, lr_lambda, last_epoch=-1)
# if resume
if self.progress['step'] > 1:
optimizer.load_state_dict(self.optim_states)
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
del self.optim_states
scheduler.load_state_dict(self.scheduler_states)
optimizer.zero_grad()
return optimizer, scheduler
def seed_everything(self, seed=1):
os.environ['PYTHONHASHSEED'] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True |