mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
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Merge pull request #183 from Lion-Wu/mps
Add MPS Backend Support for macOS
This commit is contained in:
RVC-Boss
GitHub
commit
fe16adc861
@ -41,12 +41,13 @@ class DistributedBucketSampler(Sampler[T_co]):
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if num_replicas is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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num_replicas = dist.get_world_size()
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num_replicas = dist.get_world_size() if torch.cuda.is_available() else 1
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if rank is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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rank = dist.get_rank()
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torch.cuda.set_device(rank)
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rank = dist.get_rank() if torch.cuda.is_available() else 0
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if torch.cuda.is_available():
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torch.cuda.set_device(rank)
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if rank >= num_replicas or rank < 0:
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raise ValueError(
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"Invalid rank {}, rank should be in the interval"
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@ -40,7 +40,15 @@ from my_utils import load_audio
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto()
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device = "cuda"
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os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
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if torch.cuda.is_available():
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device = "cuda"
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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "cpu"
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
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if is_half == True:
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@ -46,7 +46,7 @@ if os.path.exists(txt_path) == False:
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bert_dir = "%s/3-bert" % (opt_dir)
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os.makedirs(opt_dir, exist_ok=True)
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os.makedirs(bert_dir, exist_ok=True)
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device = "cuda:0"
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device = "cuda:0" if torch.cuda.is_available() else "mps"
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tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
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if is_half == True:
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@ -47,7 +47,7 @@ os.makedirs(wav32dir,exist_ok=True)
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maxx=0.95
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alpha=0.5
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device="cuda:0"
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device="cuda:0" if torch.cuda.is_available() else "mps"
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model=cnhubert.get_model()
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# is_half=False
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if(is_half==True):
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@ -38,7 +38,7 @@ semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
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if os.path.exists(semantic_path) == False:
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os.makedirs(opt_dir, exist_ok=True)
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device = "cuda:0"
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device = "cuda:0" if torch.cuda.is_available() else "mps"
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hps = utils.get_hparams_from_file(s2config_path)
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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@ -116,9 +116,9 @@ def main(args):
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devices=-1,
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benchmark=False,
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fast_dev_run=False,
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strategy=DDPStrategy(
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strategy = "auto" if torch.backends.mps.is_available() else DDPStrategy(
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process_group_backend="nccl" if platform.system() != "Windows" else "gloo"
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),
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), # mps 不支持多节点训练
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precision=config["train"]["precision"],
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logger=logger,
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num_sanity_val_steps=0,
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@ -44,9 +44,12 @@ global_step = 0
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def main():
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"""Assume Single Node Multi GPUs Training Only"""
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assert torch.cuda.is_available(), "CPU training is not allowed."
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assert torch.cuda.is_available() or torch.backends.mps.is_available(), "Only GPU training is allowed."
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n_gpus = torch.cuda.device_count()
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if torch.backends.mps.is_available():
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n_gpus = 1
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else:
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n_gpus = torch.cuda.device_count()
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = str(randint(20000, 55555))
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@ -70,13 +73,14 @@ def run(rank, n_gpus, hps):
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writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
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dist.init_process_group(
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backend="gloo" if os.name == "nt" else "nccl",
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backend = "gloo" if os.name == "nt" or torch.backends.mps.is_available() else "nccl",
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init_method="env://",
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world_size=n_gpus,
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rank=rank,
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)
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torch.manual_seed(hps.train.seed)
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torch.cuda.set_device(rank)
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if torch.cuda.is_available():
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torch.cuda.set_device(rank)
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train_dataset = TextAudioSpeakerLoader(hps.data) ########
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train_sampler = DistributedBucketSampler(
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@ -128,9 +132,14 @@ def run(rank, n_gpus, hps):
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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).cuda(rank)
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).cuda(rank) if torch.cuda.is_available() else SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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).to("mps")
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to("mps")
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for name, param in net_g.named_parameters():
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if not param.requires_grad:
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print(name, "not requires_grad")
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@ -174,8 +183,12 @@ def run(rank, n_gpus, hps):
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betas=hps.train.betas,
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eps=hps.train.eps,
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)
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net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
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net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
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if torch.cuda.is_available():
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net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
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net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
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else:
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net_g = net_g.to("mps")
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net_d = net_d.to("mps")
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try: # 如果能加载自动resume
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_, _, _, epoch_str = utils.load_checkpoint(
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@ -205,6 +218,9 @@ def run(rank, n_gpus, hps):
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net_g.module.load_state_dict(
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torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
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strict=False,
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) if torch.cuda.is_available() else net_g.load_state_dict(
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torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
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strict=False,
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)
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) ##测试不加载优化器
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if hps.train.pretrained_s2D != "":
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@ -213,6 +229,8 @@ def run(rank, n_gpus, hps):
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print(
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net_d.module.load_state_dict(
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torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
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) if torch.cuda.is_available() else net_d.load_state_dict(
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torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
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)
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)
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@ -288,18 +306,26 @@ def train_and_evaluate(
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text,
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text_lengths,
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) in tqdm(enumerate(train_loader)):
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spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
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rank, non_blocking=True
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)
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y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
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rank, non_blocking=True
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)
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ssl = ssl.cuda(rank, non_blocking=True)
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ssl.requires_grad = False
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# ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
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text, text_lengths = text.cuda(rank, non_blocking=True), text_lengths.cuda(
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rank, non_blocking=True
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)
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if torch.cuda.is_available():
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spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
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rank, non_blocking=True
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)
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y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
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rank, non_blocking=True
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)
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ssl = ssl.cuda(rank, non_blocking=True)
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ssl.requires_grad = False
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# ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
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text, text_lengths = text.cuda(rank, non_blocking=True), text_lengths.cuda(
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rank, non_blocking=True
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)
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else:
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spec, spec_lengths = spec.to("mps"), spec_lengths.to("mps")
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y, y_lengths = y.to("mps"), y_lengths.to("mps")
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ssl = ssl.to("mps")
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ssl.requires_grad = False
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# ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
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text, text_lengths = text.to("mps"), text_lengths.to("mps")
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with autocast(enabled=hps.train.fp16_run):
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(
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@ -500,13 +526,21 @@ def evaluate(hps, generator, eval_loader, writer_eval):
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text_lengths,
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) in enumerate(eval_loader):
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print(111)
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spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
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y, y_lengths = y.cuda(), y_lengths.cuda()
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ssl = ssl.cuda()
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text, text_lengths = text.cuda(), text_lengths.cuda()
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if torch.cuda.is_available():
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spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
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y, y_lengths = y.cuda(), y_lengths.cuda()
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ssl = ssl.cuda()
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text, text_lengths = text.cuda(), text_lengths.cuda()
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else:
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spec, spec_lengths = spec.to("mps"), spec_lengths.to("mps")
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y, y_lengths = y.to("mps"), y_lengths.to("mps")
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ssl = ssl.to("mps")
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text, text_lengths = text.to("mps"), text_lengths.to("mps")
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for test in [0, 1]:
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y_hat, mask, *_ = generator.module.infer(
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ssl, spec, spec_lengths, text, text_lengths, test=test
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) if torch.cuda.is_available() else generator.infer(
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ssl, spec, spec_lengths, text, text_lengths, test=test
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)
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y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
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24
README.md
24
README.md
@ -43,9 +43,24 @@ If you are a Windows user (tested with win>=10) you can install directly via the
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- Python 3.9, PyTorch 2.0.1, CUDA 11
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- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
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- Python 3.9, PyTorch 2.3.0.dev20240122, macOS 14.3 (Apple Silicon, MPS)
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_Note: numba==0.56.4 require py<3.11_
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### For Mac Users
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If you are a Mac user, please install by using the following commands:
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#### Create Environment
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```bash
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conda create -n GPTSoVits python=3.9
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conda activate GPTSoVits
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```
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#### Install Requirements
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```bash
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pip install -r requirements.txt
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pip uninstall torch torchaudio
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pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
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```
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_Note: For preprocessing with UVR5, it is recommended to [download the original project GUI](https://github.com/Anjok07/ultimatevocalremovergui) and select GPU for operation. Additionally, there may be memory leak issues when using Mac for inference, restarting the inference webUI can release the memory._
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### Quick Install with Conda
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```bash
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@ -58,16 +73,9 @@ bash install.sh
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#### Pip Packages
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```bash
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pip install torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet transformers jieba_fast
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pip install -r requirements.txt
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```
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#### Additional Requirements
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If you need Chinese ASR (supported by FunASR), install:
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```bash
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pip install modelscope torchaudio sentencepiece funasr
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```
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#### FFmpeg
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14
api.py
14
api.py
@ -30,25 +30,25 @@ endpoint: `/`
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使用执行参数指定的参考音频:
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GET:
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`http://127.0.0.1:9880?text=你所热爱的，就是你的生活。&text_language=zh`
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`http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
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POST:
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```json
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{
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"text": "你所热爱的，就是你的生活。",
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"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
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"text_language": "zh"
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}
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```
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手动指定当次推理所使用的参考音频:
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GET:
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`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=你所热爱的，就是你的生活。&text_language=zh`
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`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
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POST:
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```json
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{
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"refer_wav_path": "123.wav",
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"prompt_text": "一二三。",
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"prompt_language": "zh",
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"text": "你所热爱的，就是你的生活。",
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"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
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"text_language": "zh"
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}
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```
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@ -138,7 +138,8 @@ parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="
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parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
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parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
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parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
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parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu / mps")
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parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
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parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
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parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
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parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
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@ -278,7 +279,7 @@ vq_model.eval()
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print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
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hz = 50
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max_sec = config['data']['max_sec']
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t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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t2s_model.load_state_dict(dict_s1["weight"])
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if is_half:
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t2s_model = t2s_model.half()
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@ -439,6 +440,7 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
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wav.seek(0)
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torch.cuda.empty_cache()
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torch.mps.empty_cache()
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return StreamingResponse(wav, media_type="audio/wav")
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@ -1,5 +1,6 @@
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import sys,os
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import torch
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# 推理用的指定模型
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sovits_path = ""
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@ -14,7 +15,12 @@ pretrained_gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=
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exp_root = "logs"
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python_exec = sys.executable or "python"
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infer_device = "cuda"
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if torch.cuda.is_available():
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infer_device = "cuda"
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elif torch.backends.mps.is_available():
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infer_device = "mps"
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else:
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infer_device = "cpu"
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webui_port_main = 9874
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webui_port_uvr5 = 9873
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@ -38,10 +38,29 @@ https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-
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如果你是Windows用户（已在win>=10上测试），可以直接通过预打包文件安装。只需下载[预打包文件](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true)，解压后双击go-webui.bat即可启动GPT-SoVITS-WebUI。
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### Python和PyTorch版本
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已在Python 3.9、PyTorch 2.0.1和CUDA 11上测试。
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### 测试通过的Python和PyTorch版本
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- Python 3.9、PyTorch 2.0.1和CUDA 11
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- Python 3.10.13, PyTorch 2.1.2和CUDA 12.3
|
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- Python 3.9、Pytorch 2.3.0.dev20240122和macOS 14.3（Apple 芯片，MPS）
|
||||
|
||||
_注意: numba==0.56.4 需要 python<3.11_
|
||||
|
||||
### Mac 用户
|
||||
如果你是Mac用户，请使用以下命令安装：
|
||||
#### 创建环境
|
||||
```bash
|
||||
conda create -n GPTSoVits python=3.9
|
||||
conda activate GPTSoVits
|
||||
```
|
||||
#### 安装依赖
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
pip uninstall torch torchaudio
|
||||
pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
|
||||
```
|
||||
_注意：如需使用UVR5进行预处理，建议[下载原项目GUI](https://github.com/Anjok07/ultimatevocalremovergui)，勾选GPU运行。另外，使用Mac推理时可能存在内存泄漏问题，重启推理UI即可释放内存。_
|
||||
### 使用Conda快速安装
|
||||
|
||||
```bash
|
||||
@ -53,15 +72,7 @@ bash install.sh
|
||||
#### Pip包
|
||||
|
||||
```bash
|
||||
pip install torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet transformers
|
||||
```
|
||||
|
||||
#### 额外要求
|
||||
|
||||
如果你需要中文自动语音识别（由FunASR支持），请安装：
|
||||
|
||||
```bash
|
||||
pip install modelscope torchaudio sentencepiece funasr
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
#### FFmpeg
|
||||
|
||||
@ -37,9 +37,26 @@ https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-
|
||||
Windows ユーザーであれば（win>=10 にてテスト済み）、prezip 経由で直接インストールできます。[prezip](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true) をダウンロードして解凍し、go-webui.bat をダブルクリックするだけで GPT-SoVITS-WebUI が起動します。
|
||||
|
||||
### Python と PyTorch のバージョン
|
||||
- Python 3.9, PyTorch 2.0.1, CUDA 11
|
||||
- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
|
||||
- Python 3.9, PyTorch 2.3.0.dev20240122, macOS 14.3 (Apple Silicon, MPS)
|
||||
|
||||
Python 3.9、PyTorch 2.0.1、CUDA 11でテスト済。
|
||||
_注記: numba==0.56.4 は py<3.11 が必要です_
|
||||
|
||||
### Macユーザーへ
|
||||
Macユーザーの方は、以下のコマンドを使用してインストールしてください。
|
||||
#### 環境作成
|
||||
```bash
|
||||
conda create -n GPTSoVits python=3.9
|
||||
conda activate GPTSoVits
|
||||
```
|
||||
#### Pip パッケージ
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
pip uninstall torch torchaudio
|
||||
pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
|
||||
```
|
||||
_注記: UVR5を使用した前処理には、[元のプロジェクトGUIをダウンロード](https://github.com/Anjok07/ultimatevocalremovergui)して、操作にGPUを選択することを推奨します。さらに、Macを使用して推論する際にメモリリークの問題が発生する可能性がありますが、推論のwebUIを再起動することでメモリを解放できます。_
|
||||
### Conda によるクイックインストール
|
||||
|
||||
```bash
|
||||
@ -52,15 +69,7 @@ bash install.sh
|
||||
#### Pip パッケージ
|
||||
|
||||
```bash
|
||||
pip install torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet transformers
|
||||
```
|
||||
|
||||
#### 追加要件
|
||||
|
||||
中国語の ASR（FunASR がサポート）が必要な場合は、以下をインストールしてください:
|
||||
|
||||
```bash
|
||||
pip install modelscope torchaudio sentencepiece funasr
|
||||
pip install -r requirementx.txt
|
||||
```
|
||||
|
||||
#### FFmpeg
|
||||
|
||||
11
webui.py
11
webui.py
@ -45,14 +45,17 @@ i18n = I18nAuto()
|
||||
from scipy.io import wavfile
|
||||
from tools.my_utils import load_audio
|
||||
from multiprocessing import cpu_count
|
||||
|
||||
os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
|
||||
|
||||
n_cpu=cpu_count()
|
||||
|
||||
# 判断是否有能用来训练和加速推理的N卡
|
||||
ngpu = torch.cuda.device_count()
|
||||
gpu_infos = []
|
||||
mem = []
|
||||
if_gpu_ok = False
|
||||
|
||||
# 判断是否有能用来训练和加速推理的N卡
|
||||
if torch.cuda.is_available() or ngpu != 0:
|
||||
for i in range(ngpu):
|
||||
gpu_name = torch.cuda.get_device_name(i)
|
||||
@ -61,6 +64,12 @@ if torch.cuda.is_available() or ngpu != 0:
|
||||
if_gpu_ok = True # 至少有一张能用的N卡
|
||||
gpu_infos.append("%s\t%s" % (i, gpu_name))
|
||||
mem.append(int(torch.cuda.get_device_properties(i).total_memory/ 1024/ 1024/ 1024+ 0.4))
|
||||
# 判断是否支持mps加速
|
||||
if torch.backends.mps.is_available():
|
||||
if_gpu_ok = True
|
||||
gpu_infos.append("%s\t%s" % ("0", "Apple GPU"))
|
||||
mem.append(psutil.virtual_memory().total/ 1024 / 1024 / 1024) # 实测使用系统内存作为显存不会爆显存
|
||||
|
||||
if if_gpu_ok and len(gpu_infos) > 0:
|
||||
gpu_info = "\n".join(gpu_infos)
|
||||
default_batch_size = min(mem) // 2
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user