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# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
# 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from contextlib import nullcontext
import os
import torch
import torch.distributed as dist
from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, log_per_save, batch_forward, batch_backward, save_model, cosyvoice_join
class Executor:
def __init__(self):
self.step = 0
self.epoch = 0
self.rank = int(os.environ.get('RANK', 0))
self.device = torch.device('cuda:{}'.format(self.rank))
def train_one_epoc(self, model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, group_join):
''' Train one epoch
'''
lr = optimizer.param_groups[0]['lr']
logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank))
logging.info('using accumulate grad, new batch size is {} times'
' larger than before'.format(info_dict['accum_grad']))
# A context manager to be used in conjunction with an instance of
# torch.nn.parallel.DistributedDataParallel to be able to train
# with uneven inputs across participating processes.
model.train()
model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext
with model_context():
for batch_idx, batch_dict in enumerate(train_data_loader):
info_dict["tag"] = "TRAIN"
info_dict["step"] = self.step
info_dict["epoch"] = self.epoch
info_dict["batch_idx"] = batch_idx
if cosyvoice_join(group_join, info_dict):
break
# Disable gradient synchronizations across DDP processes.
# Within this context, gradients will be accumulated on module
# variables, which will later be synchronized.
if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0:
context = model.no_sync
# Used for single gpu training and DDP gradient synchronization
# processes.
else:
context = nullcontext
with context():
info_dict = batch_forward(model, batch_dict, info_dict)
info_dict = batch_backward(model, info_dict)
info_dict = update_parameter_and_lr(model, optimizer, scheduler, info_dict)
log_per_step(writer, info_dict)
# NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save
if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and (batch_idx + 1) % info_dict["accum_grad"] == 0:
dist.barrier()
self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False)
model.train()
if (batch_idx + 1) % info_dict["accum_grad"] == 0:
self.step += 1
dist.barrier()
self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
@torch.inference_mode()
def cv(self, model, cv_data_loader, writer, info_dict, on_batch_end=True):
''' Cross validation on
'''
logging.info('Epoch {} Step {} on_batch_end {} CV rank {}'.format(self.epoch, self.step + 1, on_batch_end, self.rank))
model.eval()
total_num_utts, total_loss_dict = 0, {} # avoid division by 0
for batch_idx, batch_dict in enumerate(cv_data_loader):
info_dict["tag"] = "CV"
info_dict["step"] = self.step
info_dict["epoch"] = self.epoch
info_dict["batch_idx"] = batch_idx
num_utts = len(batch_dict["utts"])
total_num_utts += num_utts
info_dict = batch_forward(model, batch_dict, info_dict)
for k, v in info_dict['loss_dict'].items():
if k not in total_loss_dict:
total_loss_dict[k] = []
total_loss_dict[k].append(v.item() * num_utts)
log_per_step(None, info_dict)
for k, v in total_loss_dict.items():
total_loss_dict[k] = sum(v) / total_num_utts
info_dict['loss_dict'] = total_loss_dict
log_per_save(writer, info_dict)
model_name = 'epoch_{}_whole'.format(self.epoch) if on_batch_end else 'epoch_{}_step_{}'.format(self.epoch, self.step + 1)
save_model(model, model_name, info_dict)
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