336 lines
11 KiB
Python
336 lines
11 KiB
Python
|
import logging
|
||
|
import os
|
||
|
import random
|
||
|
import shutil
|
||
|
import sys
|
||
|
|
||
|
import matplotlib
|
||
|
import numpy as np
|
||
|
import torch.distributed as dist
|
||
|
import torch.utils.data
|
||
|
from pytorch_lightning.loggers import TensorBoardLogger
|
||
|
from torch import nn
|
||
|
|
||
|
import utils
|
||
|
from utils.hparams import hparams, set_hparams
|
||
|
from utils.pl_utils import LatestModelCheckpoint, BaseTrainer, data_loader, DDP
|
||
|
|
||
|
matplotlib.use('Agg')
|
||
|
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(nn.Module):
|
||
|
'''
|
||
|
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. *validation_end* and *_validation_end*:
|
||
|
postprocess the validation output.
|
||
|
'''
|
||
|
|
||
|
def __init__(self, *args, **kwargs):
|
||
|
# dataset configs
|
||
|
super(BaseTask, self).__init__(*args, **kwargs)
|
||
|
self.current_epoch = 0
|
||
|
self.global_step = 0
|
||
|
self.loaded_optimizer_states_dict = {}
|
||
|
self.trainer = None
|
||
|
self.logger = None
|
||
|
self.on_gpu = False
|
||
|
self.use_dp = False
|
||
|
self.use_ddp = False
|
||
|
self.example_input_array = None
|
||
|
|
||
|
self.max_tokens = hparams['max_tokens']
|
||
|
self.max_sentences = hparams['max_sentences']
|
||
|
self.max_eval_tokens = hparams['max_eval_tokens']
|
||
|
if self.max_eval_tokens == -1:
|
||
|
hparams['max_eval_tokens'] = self.max_eval_tokens = self.max_tokens
|
||
|
self.max_eval_sentences = hparams['max_eval_sentences']
|
||
|
if self.max_eval_sentences == -1:
|
||
|
hparams['max_eval_sentences'] = self.max_eval_sentences = self.max_sentences
|
||
|
|
||
|
self.model = None
|
||
|
self.training_losses_meter = None
|
||
|
|
||
|
###########
|
||
|
# Training, validation and testing
|
||
|
###########
|
||
|
def build_model(self):
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def load_ckpt(self, ckpt_base_dir, current_model_name=None, model_name='model', force=True, strict=True):
|
||
|
# This function is updated on 2021.12.13
|
||
|
if current_model_name is None:
|
||
|
current_model_name = model_name
|
||
|
utils.load_ckpt(self.__getattr__(current_model_name), ckpt_base_dir, current_model_name, force, strict)
|
||
|
|
||
|
def on_epoch_start(self):
|
||
|
self.training_losses_meter = {'total_loss': utils.AvgrageMeter()}
|
||
|
|
||
|
def _training_step(self, sample, batch_idx, optimizer_idx):
|
||
|
"""
|
||
|
|
||
|
:param sample:
|
||
|
:param batch_idx:
|
||
|
:return: total loss: torch.Tensor, loss_log: dict
|
||
|
"""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def training_step(self, sample, batch_idx, optimizer_idx=-1):
|
||
|
loss_ret = self._training_step(sample, batch_idx, optimizer_idx)
|
||
|
self.opt_idx = optimizer_idx
|
||
|
if loss_ret is None:
|
||
|
return {'loss': None}
|
||
|
total_loss, log_outputs = loss_ret
|
||
|
log_outputs = utils.tensors_to_scalars(log_outputs)
|
||
|
for k, v in log_outputs.items():
|
||
|
if k not in self.training_losses_meter:
|
||
|
self.training_losses_meter[k] = utils.AvgrageMeter()
|
||
|
if not np.isnan(v):
|
||
|
self.training_losses_meter[k].update(v)
|
||
|
self.training_losses_meter['total_loss'].update(total_loss.item())
|
||
|
|
||
|
try:
|
||
|
log_outputs['lr'] = self.scheduler.get_lr()
|
||
|
if isinstance(log_outputs['lr'], list):
|
||
|
log_outputs['lr'] = log_outputs['lr'][0]
|
||
|
except:
|
||
|
pass
|
||
|
|
||
|
# log_outputs['all_loss'] = total_loss.item()
|
||
|
progress_bar_log = log_outputs
|
||
|
tb_log = {f'tr/{k}': v for k, v in log_outputs.items()}
|
||
|
return {
|
||
|
'loss': total_loss,
|
||
|
'progress_bar': progress_bar_log,
|
||
|
'log': tb_log
|
||
|
}
|
||
|
|
||
|
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx):
|
||
|
optimizer.step()
|
||
|
optimizer.zero_grad()
|
||
|
if self.scheduler is not None:
|
||
|
self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
|
||
|
|
||
|
def on_epoch_end(self):
|
||
|
loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()}
|
||
|
print(f"\n==============\n "
|
||
|
f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}"
|
||
|
f"\n==============\n")
|
||
|
|
||
|
def validation_step(self, sample, batch_idx):
|
||
|
"""
|
||
|
|
||
|
:param sample:
|
||
|
:param batch_idx:
|
||
|
:return: output: dict
|
||
|
"""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def _validation_end(self, outputs):
|
||
|
"""
|
||
|
|
||
|
:param outputs:
|
||
|
:return: loss_output: dict
|
||
|
"""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def validation_end(self, outputs):
|
||
|
loss_output = self._validation_end(outputs)
|
||
|
print(f"\n==============\n "
|
||
|
f"valid results: {loss_output}"
|
||
|
f"\n==============\n")
|
||
|
return {
|
||
|
'log': {f'val/{k}': v for k, v in loss_output.items()},
|
||
|
'val_loss': loss_output['total_loss']
|
||
|
}
|
||
|
|
||
|
def build_scheduler(self, optimizer):
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def build_optimizer(self, model):
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def configure_optimizers(self):
|
||
|
optm = self.build_optimizer(self.model)
|
||
|
self.scheduler = self.build_scheduler(optm)
|
||
|
return [optm]
|
||
|
|
||
|
def test_start(self):
|
||
|
pass
|
||
|
|
||
|
def test_step(self, sample, batch_idx):
|
||
|
return self.validation_step(sample, batch_idx)
|
||
|
|
||
|
def test_end(self, outputs):
|
||
|
return self.validation_end(outputs)
|
||
|
|
||
|
###########
|
||
|
# Running configuration
|
||
|
###########
|
||
|
|
||
|
@classmethod
|
||
|
def start(cls):
|
||
|
set_hparams()
|
||
|
os.environ['MASTER_PORT'] = str(random.randint(15000, 30000))
|
||
|
random.seed(hparams['seed'])
|
||
|
np.random.seed(hparams['seed'])
|
||
|
task = cls()
|
||
|
work_dir = hparams['work_dir']
|
||
|
trainer = BaseTrainer(checkpoint_callback=LatestModelCheckpoint(
|
||
|
filepath=work_dir,
|
||
|
verbose=True,
|
||
|
monitor='val_loss',
|
||
|
mode='min',
|
||
|
num_ckpt_keep=hparams['num_ckpt_keep'],
|
||
|
save_best=hparams['save_best'],
|
||
|
period=1 if hparams['save_ckpt'] else 100000
|
||
|
),
|
||
|
logger=TensorBoardLogger(
|
||
|
save_dir=work_dir,
|
||
|
name='lightning_logs',
|
||
|
version='lastest'
|
||
|
),
|
||
|
gradient_clip_val=hparams['clip_grad_norm'],
|
||
|
val_check_interval=hparams['val_check_interval'],
|
||
|
row_log_interval=hparams['log_interval'],
|
||
|
max_updates=hparams['max_updates'],
|
||
|
num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams[
|
||
|
'validate'] else 10000,
|
||
|
accumulate_grad_batches=hparams['accumulate_grad_batches'],
|
||
|
use_amp=hparams['use_amp'])
|
||
|
if not hparams['infer']: # train
|
||
|
# Copy spk_map.json to work dir
|
||
|
spk_map = os.path.join(work_dir, 'spk_map.json')
|
||
|
spk_map_orig = os.path.join(hparams['binary_data_dir'], 'spk_map.json')
|
||
|
if not os.path.exists(spk_map) and os.path.exists(spk_map_orig):
|
||
|
shutil.copy(spk_map_orig, spk_map)
|
||
|
print(f"| Copied spk map to {spk_map}.")
|
||
|
trainer.checkpoint_callback.task = task
|
||
|
trainer.fit(task)
|
||
|
else:
|
||
|
trainer.test(task)
|
||
|
|
||
|
@staticmethod
|
||
|
def configure_ddp(model, device_ids):
|
||
|
model = DDP(
|
||
|
model,
|
||
|
device_ids=device_ids,
|
||
|
find_unused_parameters=True
|
||
|
)
|
||
|
if dist.get_rank() != 0 and not hparams['debug']:
|
||
|
sys.stdout = open(os.devnull, "w")
|
||
|
sys.stderr = open(os.devnull, "w")
|
||
|
random.seed(hparams['seed'])
|
||
|
np.random.seed(hparams['seed'])
|
||
|
return model
|
||
|
|
||
|
@staticmethod
|
||
|
def training_end(self, *args, **kwargs):
|
||
|
return None
|
||
|
|
||
|
def init_ddp_connection(self, proc_rank, world_size):
|
||
|
set_hparams(print_hparams=False)
|
||
|
# guarantees unique ports across jobs from same grid search
|
||
|
default_port = 12910
|
||
|
# if user gave a port number, use that one instead
|
||
|
try:
|
||
|
default_port = os.environ['MASTER_PORT']
|
||
|
except Exception:
|
||
|
os.environ['MASTER_PORT'] = str(default_port)
|
||
|
|
||
|
# figure out the root node addr
|
||
|
root_node = '127.0.0.2'
|
||
|
root_node = self.trainer.resolve_root_node_address(root_node)
|
||
|
os.environ['MASTER_ADDR'] = root_node
|
||
|
dist.init_process_group('nccl', rank=proc_rank, world_size=world_size)
|
||
|
|
||
|
@data_loader
|
||
|
def train_dataloader(self):
|
||
|
return None
|
||
|
|
||
|
@data_loader
|
||
|
def test_dataloader(self):
|
||
|
return None
|
||
|
|
||
|
@data_loader
|
||
|
def val_dataloader(self):
|
||
|
return None
|
||
|
|
||
|
def on_load_checkpoint(self, checkpoint):
|
||
|
pass
|
||
|
|
||
|
def on_save_checkpoint(self, checkpoint):
|
||
|
pass
|
||
|
|
||
|
def on_sanity_check_start(self):
|
||
|
pass
|
||
|
|
||
|
def on_train_start(self):
|
||
|
pass
|
||
|
|
||
|
def on_train_end(self):
|
||
|
pass
|
||
|
|
||
|
def on_batch_start(self, batch):
|
||
|
pass
|
||
|
|
||
|
def on_batch_end(self):
|
||
|
pass
|
||
|
|
||
|
def on_pre_performance_check(self):
|
||
|
pass
|
||
|
|
||
|
def on_post_performance_check(self):
|
||
|
pass
|
||
|
|
||
|
def on_before_zero_grad(self, optimizer):
|
||
|
pass
|
||
|
|
||
|
def on_after_backward(self):
|
||
|
pass
|
||
|
|
||
|
@staticmethod
|
||
|
def backward(loss, optimizer):
|
||
|
loss.backward()
|
||
|
|
||
|
def grad_norm(self, norm_type):
|
||
|
results = {}
|
||
|
total_norm = 0
|
||
|
for name, p in self.named_parameters():
|
||
|
if p.requires_grad:
|
||
|
try:
|
||
|
param_norm = p.grad.data.norm(norm_type)
|
||
|
total_norm += param_norm ** norm_type
|
||
|
norm = param_norm ** (1 / norm_type)
|
||
|
|
||
|
grad = round(norm.data.cpu().numpy().flatten()[0], 3)
|
||
|
results['grad_{}_norm_{}'.format(norm_type, name)] = grad
|
||
|
except Exception:
|
||
|
# this param had no grad
|
||
|
pass
|
||
|
|
||
|
total_norm = total_norm ** (1. / norm_type)
|
||
|
grad = round(total_norm.data.cpu().numpy().flatten()[0], 3)
|
||
|
results['grad_{}_norm_total'.format(norm_type)] = grad
|
||
|
return results
|