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Merge 551d3dc2812a9f8ba8ac91cfb5ed61b7806eb530 into 08d627c3338173c3229286d8787060d6559fe0f8

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Shaoheng Lai 2026-05-16 19:11:17 -07:00 committed by GitHub
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2 changed files with 18 additions and 10 deletions
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14
GPT_SoVITS/AR/data/bucket_sampler.py
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@ -36,14 +36,16 @@ class DistributedBucketSampler(Sampler[T_co]):
drop_last: bool = False, drop_last: bool = False,
batch_size: int = 32, batch_size: int = 32,
) -> None: ) -> None:
# Patched: support non-DDP single-GPU runs (Lightning strategy='auto' on
# Windows). When the distributed group isn't initialized, fall back to
# a single-replica configuration.
_dist_ready = (
dist.is_available() and dist.is_initialized() and torch.cuda.is_available()
)
if num_replicas is None: if num_replicas is None:
if not dist.is_available(): num_replicas = dist.get_world_size() if _dist_ready else 1
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size() if torch.cuda.is_available() else 1
if rank is None: if rank is None:
if not dist.is_available(): rank = dist.get_rank() if _dist_ready else 0
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank() if torch.cuda.is_available() else 0
if torch.cuda.is_available(): if torch.cuda.is_available():
torch.cuda.set_device(rank) torch.cuda.set_device(rank)
if rank >= num_replicas or rank < 0: if rank >= num_replicas or rank < 0:

14
GPT_SoVITS/s1_train.py
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@ -114,12 +114,18 @@ def main(args):
# 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 if torch.cuda.is_available() else 1, # On Windows, force single-device (no DDP) — see strategy comment below.
# Non-Windows preserves original "all GPUs" behaviour.
devices=(1 if platform.system() == "Windows" else -1) if torch.cuda.is_available() else 1,
benchmark=False, benchmark=False,
fast_dev_run=False, fast_dev_run=False,
strategy=DDPStrategy(process_group_backend="nccl" if platform.system() != "Windows" else "gloo") # On Windows, DDPStrategy with the gloo backend crashes with native
if torch.cuda.is_available() # access violations on Blackwell (sm_120) / CUDA 12.8. Lightning's
else "auto", # "auto" strategy picks `single_device` for 1 GPU which avoids DDP
# entirely. Non-Windows behaviour is preserved (NCCL DDP).
strategy="auto"
if (platform.system() == "Windows" or not torch.cuda.is_available())
else DDPStrategy(process_group_backend="nccl"),
precision=config["train"]["precision"], precision=config["train"]["precision"],
logger=logger, logger=logger,
num_sanity_val_steps=0, num_sanity_val_steps=0,