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# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang)
# 2023 Horizon Inc. (authors: Xingchen Song)
# 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.
from contextlib import nullcontext
import logging
import os
import torch
import json
import re
import datetime
import yaml
import deepspeed
import torch.optim as optim
import torch.distributed as dist
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from torch.nn.utils import clip_grad_norm_
from deepspeed.runtime.zero.stage_1_and_2 import estimate_zero2_model_states_mem_needs_all_live
from cosyvoice.dataset.dataset import Dataset
from cosyvoice.utils.scheduler import WarmupLR, NoamHoldAnnealing, ConstantLR
def init_distributed(args):
world_size = int(os.environ.get('WORLD_SIZE', 1))
local_rank = int(os.environ.get('LOCAL_RANK', 0))
rank = int(os.environ.get('RANK', 0))
logging.info('training on multiple gpus, this gpu {}'.format(local_rank) +
', rank {}, world_size {}'.format(rank, world_size))
if args.train_engine == 'torch_ddp':
torch.cuda.set_device(local_rank)
dist.init_process_group(args.dist_backend)
else:
deepspeed.init_distributed(dist_backend=args.dist_backend)
return world_size, local_rank, rank
def init_dataset_and_dataloader(args, configs):
train_dataset = Dataset(args.train_data, data_pipeline=configs['data_pipeline'], mode='train', shuffle=True, partition=True)
cv_dataset = Dataset(args.cv_data, data_pipeline=configs['data_pipeline'], mode='train', shuffle=False, partition=False)
# do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts
train_data_loader = DataLoader(train_dataset,
batch_size=None,
pin_memory=args.pin_memory,
num_workers=args.num_workers,
prefetch_factor=args.prefetch)
cv_data_loader = DataLoader(cv_dataset,
batch_size=None,
pin_memory=args.pin_memory,
num_workers=args.num_workers,
prefetch_factor=args.prefetch)
return train_dataset, cv_dataset, train_data_loader, cv_data_loader
def check_modify_and_save_config(args, configs):
if args.train_engine == "torch_ddp":
configs['train_conf']["dtype"] = 'fp32'
else:
with open(args.deepspeed_config, 'r') as fin:
ds_configs = json.load(fin)
if "fp16" in ds_configs and ds_configs["fp16"]["enabled"]:
configs['train_conf']["dtype"] = "fp16"
elif "bf16" in ds_configs and ds_configs["bf16"]["enabled"]:
configs['train_conf']["dtype"] = "bf16"
else:
configs['train_conf']["dtype"] = "fp32"
assert ds_configs["train_micro_batch_size_per_gpu"] == 1
# if use deepspeed, override ddp config
configs['train_conf']['save_per_step'] = int(configs['train_conf']['save_per_step'] * configs['train_conf']['accum_grad'] / ds_configs["gradient_accumulation_steps"])
configs['train_conf']['accum_grad'] = ds_configs["gradient_accumulation_steps"]
configs['train_conf']['grad_clip'] = ds_configs["gradient_clipping"]
configs['train_conf']['log_interval'] = ds_configs["steps_per_print"]
return configs
def wrap_cuda_model(args, model):
local_world_size = int(os.environ.get('LOCAL_WORLD_SIZE', 1))
world_size = int(os.environ.get('WORLD_SIZE', 1))
if args.train_engine == "torch_ddp": # native pytorch ddp
assert (torch.cuda.is_available())
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
else:
if int(os.environ.get('RANK', 0)) == 0:
logging.info("Estimating model states memory needs (zero2)...")
estimate_zero2_model_states_mem_needs_all_live(
model,
num_gpus_per_node=local_world_size,
num_nodes=world_size // local_world_size)
return model
def init_optimizer_and_scheduler(args, configs, model):
if configs['train_conf']['optim'] == 'adam':
optimizer = optim.Adam(model.parameters(), **configs['train_conf']['optim_conf'])
elif configs['train_conf']['optim'] == 'adamw':
optimizer = optim.AdamW(model.parameters(), **configs['train_conf']['optim_conf'])
else:
raise ValueError("unknown optimizer: " + configs['train_conf'])
if configs['train_conf']['scheduler'] == 'warmuplr':
scheduler_type = WarmupLR
scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf'])
elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing':
scheduler_type = NoamHoldAnnealing
scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf'])
elif configs['train_conf']['scheduler'] == 'constantlr':
scheduler_type = ConstantLR
scheduler = ConstantLR(optimizer)
else:
raise ValueError("unknown scheduler: " + configs['train_conf'])
# use deepspeed optimizer for speedup
if args.train_engine == "deepspeed":
def scheduler(opt):
return scheduler_type(opt, **configs['train_conf']['scheduler_conf'])
model, optimizer, _, scheduler = deepspeed.initialize(
args=args,
model=model,
optimizer=None,
lr_scheduler=scheduler,
model_parameters=model.parameters())
return model, optimizer, scheduler
def init_summarywriter(args):
writer = None
if int(os.environ.get('RANK', 0)) == 0:
os.makedirs(args.model_dir, exist_ok=True)
writer = SummaryWriter(args.tensorboard_dir)
return writer
def save_model(model, model_name, info_dict):
rank = int(os.environ.get('RANK', 0))
model_dir = info_dict["model_dir"]
save_model_path = os.path.join(model_dir, '{}.pt'.format(model_name))
if info_dict["train_engine"] == "torch_ddp":
if rank == 0:
torch.save(model.module.state_dict(), save_model_path)
else:
with torch.no_grad():
model.save_checkpoint(save_dir=model_dir,
tag=model_name,
client_state=info_dict)
if rank == 0:
info_path = re.sub('.pt$', '.yaml', save_model_path)
info_dict['save_time'] = datetime.datetime.now().strftime('%d/%m/%Y %H:%M:%S')
with open(info_path, 'w') as fout:
data = yaml.dump(info_dict)
fout.write(data)
logging.info('[Rank {}] Checkpoint: save to checkpoint {}'.format(rank, save_model_path))
def cosyvoice_join(group_join, info_dict):
world_size = int(os.environ.get('WORLD_SIZE', 1))
local_rank = int(os.environ.get('LOCAL_RANK', 0))
rank = int(os.environ.get('RANK', 0))
if info_dict["batch_idx"] != 0:
# we try to join all rank in both ddp and deepspeed mode, in case different rank has different lr
try:
dist.monitored_barrier(group=group_join,
timeout=group_join.options._timeout)
return False
except RuntimeError as e:
logging.info("Detected uneven workload distribution: {}\n".format(e) +
"Break current worker to manually join all workers, " +
"world_size {}, current rank {}, current local_rank {}\n".
format(world_size, rank, local_rank))
return True
else:
return False
def batch_forward(model, batch, info_dict):
device = int(os.environ.get('LOCAL_RANK', 0))
dtype = info_dict["dtype"]
if dtype == "fp16":
dtype = torch.float16
elif dtype == "bf16":
dtype = torch.bfloat16
else: # fp32
dtype = torch.float32
if info_dict['train_engine'] == 'torch_ddp':
autocast = nullcontext()
else:
autocast = torch.cuda.amp.autocast(enabled=True, dtype=dtype, cache_enabled=False)
with autocast:
info_dict['loss_dict'] = model(batch, device)
return info_dict
def batch_backward(model, info_dict):
if info_dict["train_engine"] == "deepspeed":
scaled_loss = model.backward(info_dict['loss_dict']['loss'])
else:
scaled_loss = info_dict['loss_dict']['loss'] / info_dict['accum_grad']
scaled_loss.backward()
info_dict['loss_dict']['loss'] = scaled_loss
return info_dict
def update_parameter_and_lr(model, optimizer, scheduler, info_dict):
grad_norm = 0.0
if info_dict['train_engine'] == "deepspeed":
info_dict["is_gradient_accumulation_boundary"] = model.is_gradient_accumulation_boundary()
model.step()
grad_norm = model.get_global_grad_norm()
elif (info_dict['batch_idx'] + 1) % info_dict["accum_grad"] == 0:
grad_norm = clip_grad_norm_(model.parameters(), info_dict['grad_clip'])
if torch.isfinite(grad_norm):
optimizer.step()
optimizer.zero_grad()
scheduler.step()
info_dict["lr"] = optimizer.param_groups[0]['lr']
info_dict["grad_norm"] = grad_norm
return info_dict
def log_per_step(writer, info_dict):
tag = info_dict["tag"]
epoch = info_dict.get('epoch', 0)
step = info_dict["step"]
batch_idx = info_dict["batch_idx"]
loss_dict = info_dict['loss_dict']
rank = int(os.environ.get('RANK', 0))
# only rank 0 write to tensorboard to avoid multi-process write
if writer is not None:
if (info_dict['train_engine'] == 'deepspeed' and info_dict['is_gradient_accumulation_boundary'] is True) or \
(info_dict['train_engine'] == 'torch_ddp' and (info_dict['batch_idx'] + 1) % info_dict['accum_grad'] == 0):
for k in ['epoch', 'lr', 'grad_norm']:
writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1)
for k, v in loss_dict.items():
writer.add_scalar('{}/{}'.format(tag, k), v, step + 1)
# TRAIN & CV, Shell log (stdout)
if (info_dict['batch_idx'] + 1) % info_dict['log_interval'] == 0:
log_str = '{} Batch {}/{} '.format(tag, epoch, batch_idx + 1)
for name, value in loss_dict.items():
log_str += '{} {:.6f} '.format(name, value)
if tag == "TRAIN":
log_str += 'lr {:.8f} grad_norm {:.6f}'.format(
info_dict["lr"], info_dict['grad_norm'])
log_str += ' rank {}'.format(rank)
logging.debug(log_str)
def log_per_save(writer, info_dict):
tag = info_dict["tag"]
epoch = info_dict["epoch"]
step = info_dict["step"]
loss_dict = info_dict["loss_dict"]
lr = info_dict['lr']
rank = int(os.environ.get('RANK', 0))
logging.info(
'Epoch {} Step {} CV info lr {} {} rank {}'.format(
epoch, step + 1, lr, rank, ' '.join(['{}_{}'.format(k, v) for k, v in loss_dict.items()])))
if writer is not None:
for k in ['epoch', 'lr']:
writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1)
for k, v in loss_dict.items():
writer.add_scalar('{}/{}'.format(tag, k), v, step + 1)
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