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Merge 4820d5a101a5240b74657a55d8b75357a04d8ce3 into 2d9193b0d3c0eae0c3a14d8c68a839f1bae157dc

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FAN JIALI 2026-03-05 03:44:28 +00:00 committed by GitHub
commit c0fe483288
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6 changed files with 74 additions and 29 deletions
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6
GPT_SoVITS/AR/data/bucket_sampler.py
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@ -39,12 +39,12 @@ class DistributedBucketSampler(Sampler[T_co]):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size() if torch.cuda.is_available() else 1
num_replicas = dist.get_world_size() if dist.is_initialized() else 1
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank() if torch.cuda.is_available() else 0
if torch.cuda.is_available():
rank = dist.get_rank() if dist.is_initialized() else 0
if torch.cuda.is_available() and dist.is_initialized():
torch.cuda.set_device(rank)
if rank >= num_replicas or rank < 0:
raise ValueError("Invalid rank {}, rank should be in the interval [0, {}]".format(rank, num_replicas - 1))

2
GPT_SoVITS/s1_train.py
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@ -118,7 +118,7 @@ def main(args):
benchmark=False,
fast_dev_run=False,
strategy=DDPStrategy(process_group_backend="nccl" if platform.system() != "Windows" else "gloo")
if torch.cuda.is_available()
if torch.cuda.is_available() and torch.cuda.device_count() > 1
else "auto",
precision=config["train"]["precision"],
logger=logger,

27
GPT_SoVITS/s2_train.py
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@ -77,12 +77,13 @@ def run(rank, n_gpus, hps):
writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
dist.init_process_group(
backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
init_method="env://?use_libuv=False",
world_size=n_gpus,
rank=rank,
)
if not (os.name == "nt" and n_gpus == 1):
dist.init_process_group(
backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
init_method="env://?use_libuv=False",
world_size=n_gpus,
rank=rank,
)
torch.manual_seed(hps.train.seed)
if torch.cuda.is_available():
torch.cuda.set_device(rank)
@ -197,8 +198,18 @@ def run(rank, n_gpus, hps):
eps=hps.train.eps,
)
if torch.cuda.is_available():
net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
if os.name == "nt" and n_gpus == 1:
class DummyDDP(torch.nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, *args, **kwargs):
return self.module(*args, **kwargs)
net_g = DummyDDP(net_g)
net_d = DummyDDP(net_d)
else:
net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
else:
net_g = net_g.to(device)
net_d = net_d.to(device)

27
GPT_SoVITS/s2_train_v3.py
View File

@ -77,12 +77,13 @@ def run(rank, n_gpus, hps):
writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
dist.init_process_group(
backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
init_method="env://?use_libuv=False",
world_size=n_gpus,
rank=rank,
)
if not (os.name == "nt" and n_gpus == 1):
dist.init_process_group(
backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
init_method="env://?use_libuv=False",
world_size=n_gpus,
rank=rank,
)
torch.manual_seed(hps.train.seed)
if torch.cuda.is_available():
torch.cuda.set_device(rank)
@ -166,8 +167,18 @@ def run(rank, n_gpus, hps):
# eps=hps.train.eps,
# )
if torch.cuda.is_available():
net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
# net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
if os.name == "nt" and n_gpus == 1:
class DummyDDP(torch.nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, *args, **kwargs):
return self.module(*args, **kwargs)
net_g = DummyDDP(net_g)
# net_d = DummyDDP(net_d)
else:
net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
# net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
else:
net_g = net_g.to(device)
# net_d = net_d.to(device)

24
GPT_SoVITS/s2_train_v3_lora.py
View File

@ -77,12 +77,13 @@ def run(rank, n_gpus, hps):
writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
dist.init_process_group(
backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
init_method="env://?use_libuv=False",
world_size=n_gpus,
rank=rank,
)
if not (os.name == "nt" and n_gpus == 1):
dist.init_process_group(
backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
init_method="env://?use_libuv=False",
world_size=n_gpus,
rank=rank,
)
torch.manual_seed(hps.train.seed)
if torch.cuda.is_available():
torch.cuda.set_device(rank)
@ -156,7 +157,16 @@ def run(rank, n_gpus, hps):
def model2cuda(net_g, rank):
if torch.cuda.is_available():
net_g = DDP(net_g.cuda(rank), device_ids=[rank], find_unused_parameters=True)
if os.name == "nt" and n_gpus == 1:
class DummyDDP(torch.nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, *args, **kwargs):
return self.module(*args, **kwargs)
net_g = DummyDDP(net_g.cuda(rank))
else:
net_g = DDP(net_g.cuda(rank), device_ids=[rank], find_unused_parameters=True)
else:
net_g = net_g.to(device)
return net_g

17
GPT_SoVITS/utils.py
View File

@ -64,12 +64,25 @@ import shutil
from time import time as ttime
import time
def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
dir = os.path.dirname(path)
name = os.path.basename(path)
tmp_path = "%s.pth" % (ttime())
tmp_path = "%s.pth" % (time.time())
torch.save(fea, tmp_path)
shutil.move(tmp_path, "%s/%s" % (dir, name))
target_path = "%s/%s" % (dir, name)
try:
shutil.move(tmp_path, target_path)
except Exception as e:
print(f"Move failed with error {e}, retrying via copy and delete...")
if os.path.exists(target_path):
try:
os.remove(target_path)
except:
pass
shutil.copyfile(tmp_path, target_path)
os.remove(tmp_path)
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):