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增加对 Linux 下纯 CPU 训练的支持

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lalala-233 2024-02-03 15:42:23 +08:00
parent 372a2ff89a
commit d3d5694ed8
2 changed files with 18 additions and 21 deletions
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6
GPT_SoVITS/s1_train.py
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@ -108,15 +108,15 @@ def main(args):
logger = TensorBoardLogger(name=output_dir.stem, save_dir=output_dir) logger = TensorBoardLogger(name=output_dir.stem, save_dir=output_dir)
trainer: Trainer = Trainer( trainer: Trainer = Trainer(
max_epochs=config["train"]["epochs"], max_epochs=config["train"]["epochs"],
accelerator="gpu", accelerator="cpu",
# val_check_interval=9999999999999999999999,###不要验证 # val_check_interval=9999999999999999999999,###不要验证
# check_val_every_n_epoch=None, # check_val_every_n_epoch=None,
limit_val_batches=0, limit_val_batches=0,
devices=-1, devices=1,
benchmark=False, benchmark=False,
fast_dev_run=False, fast_dev_run=False,
strategy = "auto" if torch.backends.mps.is_available() else DDPStrategy( strategy = "auto" if torch.backends.mps.is_available() else DDPStrategy(
process_group_backend="nccl" if platform.system() != "Windows" else "gloo" process_group_backend="nccl" if platform.system() != "Windows" and torch.cuda.is_available() else "gloo"
), # mps 不支持多节点训练 ), # mps 不支持多节点训练
precision=config["train"]["precision"], precision=config["train"]["precision"],
logger=logger, logger=logger,

33
GPT_SoVITS/s2_train.py
View File

@ -44,12 +44,9 @@ global_step = 0
def main(): def main():
"""Assume Single Node Multi GPUs Training Only""" """Assume Single Node Multi GPUs Training Only"""
assert torch.cuda.is_available() or torch.backends.mps.is_available(), "Only GPU training is allowed."
if torch.backends.mps.is_available():
n_gpus = 1
else:
n_gpus = torch.cuda.device_count() n_gpus = torch.cuda.device_count()
if n_gpus == 0:
n_gpus = 1
os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(randint(20000, 55555)) os.environ["MASTER_PORT"] = str(randint(20000, 55555))
@ -73,7 +70,7 @@ def run(rank, n_gpus, hps):
writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval")) writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
dist.init_process_group( dist.init_process_group(
backend = "gloo" if os.name == "nt" or torch.backends.mps.is_available() else "nccl", backend = "gloo" if os.name == "nt" or torch.cuda.is_available() == False or torch.backends.mps.is_available() else "nccl",
init_method="env://", init_method="env://",
world_size=n_gpus, world_size=n_gpus,
rank=rank, rank=rank,
@ -137,9 +134,9 @@ def run(rank, n_gpus, hps):
hps.train.segment_size // hps.data.hop_length, hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers, n_speakers=hps.data.n_speakers,
**hps.model, **hps.model,
).to("mps") ).to("cpu")
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to("mps") net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to("cpu")
for name, param in net_g.named_parameters(): for name, param in net_g.named_parameters():
if not param.requires_grad: if not param.requires_grad:
print(name, "not requires_grad") print(name, "not requires_grad")
@ -187,8 +184,8 @@ def run(rank, n_gpus, hps):
net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
else: else:
net_g = net_g.to("mps") net_g = net_g.to("cpu")
net_d = net_d.to("mps") net_d = net_d.to("cpu")
try: # 如果能加载自动resume try: # 如果能加载自动resume
_, _, _, epoch_str = utils.load_checkpoint( _, _, _, epoch_str = utils.load_checkpoint(
@ -320,12 +317,12 @@ def train_and_evaluate(
rank, non_blocking=True rank, non_blocking=True
) )
else: else:
spec, spec_lengths = spec.to("mps"), spec_lengths.to("mps") spec, spec_lengths = spec.to("cpu"), spec_lengths.to("cpu")
y, y_lengths = y.to("mps"), y_lengths.to("mps") y, y_lengths = y.to("cpu"), y_lengths.to("cpu")
ssl = ssl.to("mps") ssl = ssl.to("cpu")
ssl.requires_grad = False ssl.requires_grad = False
# ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True) # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
text, text_lengths = text.to("mps"), text_lengths.to("mps") text, text_lengths = text.to("cpu"), text_lengths.to("cpu")
with autocast(enabled=hps.train.fp16_run): with autocast(enabled=hps.train.fp16_run):
( (
@ -532,10 +529,10 @@ def evaluate(hps, generator, eval_loader, writer_eval):
ssl = ssl.cuda() ssl = ssl.cuda()
text, text_lengths = text.cuda(), text_lengths.cuda() text, text_lengths = text.cuda(), text_lengths.cuda()
else: else:
spec, spec_lengths = spec.to("mps"), spec_lengths.to("mps") spec, spec_lengths = spec.to("cpu"), spec_lengths.to("cpu")
y, y_lengths = y.to("mps"), y_lengths.to("mps") y, y_lengths = y.to("cpu"), y_lengths.to("cpu")
ssl = ssl.to("mps") ssl = ssl.to("cpu")
text, text_lengths = text.to("mps"), text_lengths.to("mps") text, text_lengths = text.to("cpu"), text_lengths.to("cpu")
for test in [0, 1]: for test in [0, 1]:
y_hat, mask, *_ = generator.module.infer( y_hat, mask, *_ = generator.module.infer(
ssl, spec, spec_lengths, text, text_lengths, test=test ssl, spec, spec_lengths, text, text_lengths, test=test