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import logging
import os
import pathlib
import shutil
import sys
from datetime import datetime
from typing import Dict
import matplotlib
import utils
from utils.text_encoder import TokenTextEncoder
matplotlib.use('Agg')
import torch.utils.data
from torchmetrics import Metric, MeanMetric
import lightning.pytorch as pl
from lightning.pytorch.callbacks import LearningRateMonitor
from lightning.pytorch.utilities.rank_zero import rank_zero_debug, rank_zero_info, rank_zero_only
from basics.base_module import CategorizedModule
from utils.hparams import hparams
from utils.training_utils import (
DsModelCheckpoint, DsTQDMProgressBar,
DsBatchSampler, DsTensorBoardLogger,
get_latest_checkpoint_path, get_strategy
)
from utils.phoneme_utils import locate_dictionary, build_phoneme_list
torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system'))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
class BaseTask(pl.LightningModule):
"""
Base class for training tasks.
1. *load_ckpt*:
load checkpoint;
2. *training_step*:
record and log the loss;
3. *optimizer_step*:
run backwards step;
4. *start*:
load training configs, backup code, log to tensorboard, start training;
5. *configure_ddp* and *init_ddp_connection*:
start parallel training.
Subclasses should define:
1. *build_model*, *build_optimizer*, *build_scheduler*:
how to build the model, the optimizer and the training scheduler;
2. *_training_step*:
one training step of the model;
3. *on_validation_end* and *_on_validation_end*:
postprocess the validation output.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.max_batch_frames = hparams['max_batch_frames']
self.max_batch_size = hparams['max_batch_size']
self.max_val_batch_frames = hparams['max_val_batch_frames']
if self.max_val_batch_frames == -1:
hparams['max_val_batch_frames'] = self.max_val_batch_frames = self.max_batch_frames
self.max_val_batch_size = hparams['max_val_batch_size']
if self.max_val_batch_size == -1:
hparams['max_val_batch_size'] = self.max_val_batch_size = self.max_batch_size
self.training_sampler = None
self.skip_immediate_validation = False
self.skip_immediate_ckpt_save = False
self.phone_encoder = self.build_phone_encoder()
self.build_model()
self.valid_losses: Dict[str, Metric] = {}
self.valid_metrics: Dict[str, Metric] = {}
def _finish_init(self):
self.register_validation_loss('total_loss')
self.build_losses_and_metrics()
###########
# Training, validation and testing
###########
def setup(self, stage):
self.train_dataset = self.dataset_cls(hparams['train_set_name'])
self.valid_dataset = self.dataset_cls(hparams['valid_set_name'])
self.num_replicas = (self.trainer.distributed_sampler_kwargs or {}).get('num_replicas', 1)
def get_need_freeze_state_dict_key(self, model_state_dict) -> list:
key_list = []
for i in hparams['frozen_params']:
for j in model_state_dict:
if j.startswith(i):
key_list.append(j)
return list(set(key_list))
def freeze_params(self) -> None:
model_state_dict = self.state_dict().keys()
freeze_key = self.get_need_freeze_state_dict_key(model_state_dict=model_state_dict)
for i in freeze_key:
params=self.get_parameter(i)
params.requires_grad = False
def unfreeze_all_params(self) -> None:
for i in self.model.parameters():
i.requires_grad = True
def load_finetune_ckpt(
self, state_dict
) -> None:
adapt_shapes = hparams['finetune_strict_shapes']
if not adapt_shapes:
cur_model_state_dict = self.state_dict()
unmatched_keys = []
for key, param in state_dict.items():
if key in cur_model_state_dict:
new_param = cur_model_state_dict[key]
if new_param.shape != param.shape:
unmatched_keys.append(key)
print('| Unmatched keys: ', key, new_param.shape, param.shape)
for key in unmatched_keys:
del state_dict[key]
self.load_state_dict(state_dict, strict=False)
def load_pre_train_model(self):
pre_train_ckpt_path = hparams.get('finetune_ckpt_path')
blacklist = hparams.get('finetune_ignored_params')
# whitelist=hparams.get('pre_train_whitelist')
if blacklist is None:
blacklist = []
# if whitelist is None:
# raise RuntimeError("")
if pre_train_ckpt_path is not None:
ckpt = torch.load(pre_train_ckpt_path)
# if ckpt.get('category') is None:
# raise RuntimeError("")
if isinstance(self.model, CategorizedModule):
self.model.check_category(ckpt.get('category'))
state_dict = {}
for i in ckpt['state_dict']:
# if 'diffusion' in i:
# if i in rrrr:
# continue
skip = False
for b in blacklist:
if i.startswith(b):
skip = True
break
if skip:
continue
state_dict[i] = ckpt['state_dict'][i]
print(i)
return state_dict
else:
raise RuntimeError("")
@staticmethod
def build_phone_encoder():
phone_list = build_phoneme_list()
return TokenTextEncoder(vocab_list=phone_list)
def _build_model(self):
raise NotImplementedError()
def build_model(self):
self.model = self._build_model()
# utils.load_warp(self)
self.unfreeze_all_params()
if hparams['freezing_enabled']:
self.freeze_params()
if hparams['finetune_enabled'] and get_latest_checkpoint_path(pathlib.Path(hparams['work_dir'])) is None:
self.load_finetune_ckpt(self.load_pre_train_model())
self.print_arch()
@rank_zero_only
def print_arch(self):
utils.print_arch(self.model)
def build_losses_and_metrics(self):
raise NotImplementedError()
def register_validation_metric(self, name: str, metric: Metric):
assert isinstance(metric, Metric)
self.valid_metrics[name] = metric
def register_validation_loss(self, name: str, Aggregator: Metric = MeanMetric):
assert issubclass(Aggregator, Metric)
self.valid_losses[name] = Aggregator()
def run_model(self, sample, infer=False):
"""
steps:
1. run the full model
2. calculate losses if not infer
"""
raise NotImplementedError()
def on_train_epoch_start(self):
if self.training_sampler is not None:
self.training_sampler.set_epoch(self.current_epoch)
def _training_step(self, sample):
"""
:return: total loss: torch.Tensor, loss_log: dict, other_log: dict
"""
losses = self.run_model(sample)
total_loss = sum(losses.values())
return total_loss, {**losses, 'batch_size': float(sample['size'])}
def training_step(self, sample, batch_idx):
total_loss, log_outputs = self._training_step(sample)
# logs to progress bar
self.log_dict(log_outputs, prog_bar=True, logger=False, on_step=True, on_epoch=False)
self.log('lr', self.lr_schedulers().get_last_lr()[0], prog_bar=True, logger=False, on_step=True, on_epoch=False)
# logs to tensorboard
if self.global_step % hparams['log_interval'] == 0:
tb_log = {f'training/{k}': v for k, v in log_outputs.items()}
tb_log['training/lr'] = self.lr_schedulers().get_last_lr()[0]
self.logger.log_metrics(tb_log, step=self.global_step)
return total_loss
# def on_before_optimizer_step(self, *args, **kwargs):
# self.log_dict(grad_norm(self, norm_type=2))
def _on_validation_start(self):
pass
def on_validation_start(self):
if self.skip_immediate_validation:
rank_zero_debug("Skip validation")
return
self._on_validation_start()
for metric in self.valid_losses.values():
metric.to(self.device)
metric.reset()
for metric in self.valid_metrics.values():
metric.to(self.device)
metric.reset()
def _validation_step(self, sample, batch_idx):
"""
:param sample:
:param batch_idx:
:return: loss_log: dict, weight: int
"""
raise NotImplementedError()
def validation_step(self, sample, batch_idx):
"""
:param sample:
:param batch_idx:
"""
if self.skip_immediate_validation:
rank_zero_debug("Skip validation")
return
if sample['size'] > 0:
with torch.autocast(self.device.type, enabled=False):
losses, weight = self._validation_step(sample, batch_idx)
losses = {
'total_loss': sum(losses.values()),
**losses
}
for k, v in losses.items():
self.valid_losses[k].update(v, weight=weight)
def _on_validation_epoch_end(self):
pass
def on_validation_epoch_end(self):
if self.skip_immediate_validation:
self.skip_immediate_validation = False
self.skip_immediate_ckpt_save = True
return
self._on_validation_epoch_end()
loss_vals = {k: v.compute() for k, v in self.valid_losses.items()}
metric_vals = {k: v.compute() for k, v in self.valid_metrics.items()}
self.log('val_loss', loss_vals['total_loss'], on_epoch=True, prog_bar=True, logger=False, sync_dist=True)
self.logger.log_metrics({f'validation/{k}': v for k, v in loss_vals.items()}, step=self.global_step)
self.logger.log_metrics({f'metrics/{k}': v for k, v in metric_vals.items()}, step=self.global_step)
# noinspection PyMethodMayBeStatic
def build_scheduler(self, optimizer):
from utils import build_lr_scheduler_from_config
scheduler_args = hparams['lr_scheduler_args']
assert scheduler_args['scheduler_cls'] != ''
scheduler = build_lr_scheduler_from_config(optimizer, scheduler_args)
return scheduler
# noinspection PyMethodMayBeStatic
def build_optimizer(self, model):
from utils import build_object_from_class_name
optimizer_args = hparams['optimizer_args']
assert optimizer_args['optimizer_cls'] != ''
if 'beta1' in optimizer_args and 'beta2' in optimizer_args and 'betas' not in optimizer_args:
optimizer_args['betas'] = (optimizer_args['beta1'], optimizer_args['beta2'])
optimizer = build_object_from_class_name(
optimizer_args['optimizer_cls'],
torch.optim.Optimizer,
model.parameters(),
**optimizer_args
)
return optimizer
def configure_optimizers(self):
optm = self.build_optimizer(self.model)
scheduler = self.build_scheduler(optm)
if scheduler is None:
return optm
return {
"optimizer": optm,
"lr_scheduler": {
"scheduler": scheduler,
"interval": "step",
"frequency": 1
}
}
def train_dataloader(self):
self.training_sampler = DsBatchSampler(
self.train_dataset,
max_batch_frames=self.max_batch_frames,
max_batch_size=self.max_batch_size,
num_replicas=self.num_replicas,
rank=self.global_rank,
sort_by_similar_size=hparams['sort_by_len'],
size_reversed=True,
required_batch_count_multiple=hparams['accumulate_grad_batches'],
shuffle_sample=True,
shuffle_batch=True,
seed=hparams['seed']
)
return torch.utils.data.DataLoader(
self.train_dataset,
collate_fn=self.train_dataset.collater,
batch_sampler=self.training_sampler,
num_workers=hparams['ds_workers'],
prefetch_factor=hparams['dataloader_prefetch_factor'],
pin_memory=True,
persistent_workers=True
)
def val_dataloader(self):
sampler = DsBatchSampler(
self.valid_dataset,
max_batch_frames=self.max_val_batch_frames,
max_batch_size=self.max_val_batch_size,
num_replicas=self.num_replicas,
rank=self.global_rank,
shuffle_sample=False,
shuffle_batch=False,
disallow_empty_batch=False,
pad_batch_assignment=False
)
return torch.utils.data.DataLoader(
self.valid_dataset,
collate_fn=self.valid_dataset.collater,
batch_sampler=sampler,
num_workers=hparams['ds_workers'],
prefetch_factor=hparams['dataloader_prefetch_factor'],
persistent_workers=True
)
def test_dataloader(self):
return self.val_dataloader()
def on_test_start(self):
self.on_validation_start()
def test_step(self, sample, batch_idx):
return self.validation_step(sample, batch_idx)
def on_test_end(self):
return self.on_validation_end()
###########
# Running configuration
###########
@classmethod
def start(cls):
pl.seed_everything(hparams['seed'], workers=True)
task = cls()
# if pre_train is not None:
# task.load_state_dict(pre_train,strict=False)
# print("load success-------------------------------------------------------------------")
work_dir = pathlib.Path(hparams['work_dir'])
trainer = pl.Trainer(
accelerator=hparams['pl_trainer_accelerator'],
devices=hparams['pl_trainer_devices'],
num_nodes=hparams['pl_trainer_num_nodes'],
strategy=get_strategy(hparams['pl_trainer_strategy']),
precision=hparams['pl_trainer_precision'],
callbacks=[
DsModelCheckpoint(
dirpath=work_dir,
filename='model_ckpt_steps_{step}',
auto_insert_metric_name=False,
monitor='step',
mode='max',
save_last=False,
# every_n_train_steps=hparams['val_check_interval'],
save_top_k=hparams['num_ckpt_keep'],
permanent_ckpt_start=hparams['permanent_ckpt_start'],
permanent_ckpt_interval=hparams['permanent_ckpt_interval'],
verbose=True
),
# LearningRateMonitor(logging_interval='step'),
DsTQDMProgressBar(),
],
logger=DsTensorBoardLogger(
save_dir=str(work_dir),
name='lightning_logs',
version='latest'
),
gradient_clip_val=hparams['clip_grad_norm'],
val_check_interval=hparams['val_check_interval'] * hparams['accumulate_grad_batches'],
# so this is global_steps
check_val_every_n_epoch=None,
log_every_n_steps=1,
max_steps=hparams['max_updates'],
use_distributed_sampler=False,
num_sanity_val_steps=hparams['num_sanity_val_steps'],
accumulate_grad_batches=hparams['accumulate_grad_batches']
)
if not hparams['infer']: # train
@rank_zero_only
def train_payload_copy():
# copy_code = input(f'{hparams["save_codes"]} code backup? y/n: ') == 'y'
copy_code = True # backup code every time
if copy_code:
code_dir = work_dir / 'codes' / datetime.now().strftime('%Y%m%d%H%M%S')
code_dir.mkdir(exist_ok=True, parents=True)
for c in hparams['save_codes']:
shutil.copytree(c, code_dir / c, dirs_exist_ok=True)
print(f'| Copied codes to {code_dir}.')
# Copy spk_map.json and dictionary.txt to work dir
binary_dir = pathlib.Path(hparams['binary_data_dir'])
spk_map = work_dir / 'spk_map.json'
spk_map_src = binary_dir / 'spk_map.json'
if not spk_map.exists() and spk_map_src.exists():
shutil.copy(spk_map_src, spk_map)
print(f'| Copied spk map to {spk_map}.')
dictionary = work_dir / 'dictionary.txt'
dict_src = binary_dir / 'dictionary.txt'
if not dictionary.exists():
if dict_src.exists():
shutil.copy(dict_src, dictionary)
else:
shutil.copy(locate_dictionary(), dictionary)
print(f'| Copied dictionary to {dictionary}.')
train_payload_copy()
trainer.fit(task, ckpt_path=get_latest_checkpoint_path(work_dir))
else:
trainer.test(task)
def on_save_checkpoint(self, checkpoint):
if isinstance(self.model, CategorizedModule):
checkpoint['category'] = self.model.category
checkpoint['trainer_stage'] = self.trainer.state.stage.value
def on_load_checkpoint(self, checkpoint):
from lightning.pytorch.trainer.states import RunningStage
from utils import simulate_lr_scheduler
if checkpoint.get('trainer_stage', '') == RunningStage.VALIDATING.value:
self.skip_immediate_validation = True
optimizer_args = hparams['optimizer_args']
scheduler_args = hparams['lr_scheduler_args']
if 'beta1' in optimizer_args and 'beta2' in optimizer_args and 'betas' not in optimizer_args:
optimizer_args['betas'] = (optimizer_args['beta1'], optimizer_args['beta2'])
if checkpoint.get('optimizer_states', None):
opt_states = checkpoint['optimizer_states']
assert len(opt_states) == 1 # only support one optimizer
opt_state = opt_states[0]
for param_group in opt_state['param_groups']:
for k, v in optimizer_args.items():
if k in param_group and param_group[k] != v:
if 'lr_schedulers' in checkpoint and checkpoint['lr_schedulers'] and k == 'lr':
continue
rank_zero_info(f'| Overriding optimizer parameter {k} from checkpoint: {param_group[k]} -> {v}')
param_group[k] = v
if 'initial_lr' in param_group and param_group['initial_lr'] != optimizer_args['lr']:
rank_zero_info(
f'| Overriding optimizer parameter initial_lr from checkpoint: {param_group["initial_lr"]} -> {optimizer_args["lr"]}'
)
param_group['initial_lr'] = optimizer_args['lr']
if checkpoint.get('lr_schedulers', None):
assert checkpoint.get('optimizer_states', False)
assert len(checkpoint['lr_schedulers']) == 1 # only support one scheduler
checkpoint['lr_schedulers'][0] = simulate_lr_scheduler(
optimizer_args, scheduler_args,
step_count=checkpoint['global_step'],
num_param_groups=len(checkpoint['optimizer_states'][0]['param_groups'])
)
for param_group, new_lr in zip(
checkpoint['optimizer_states'][0]['param_groups'],
checkpoint['lr_schedulers'][0]['_last_lr'],
):
if param_group['lr'] != new_lr:
rank_zero_info(f'| Overriding optimizer parameter lr from checkpoint: {param_group["lr"]} -> {new_lr}')
param_group['lr'] = new_lr