support cpu training, use cpu training on mac

GPT_SoVITS/inference_webui.py

@@ -49,7 +49,7 @@
 is_share = eval(is_share)
 if "_CUDA_VISIBLE_DEVICES" in os.environ:
     os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
-is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available()
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 import gradio as gr
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 import numpy as np
@@ -69,7 +69,7 @@
 
 i18n = I18nAuto()
 
-os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。
+# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。
 
 if torch.cuda.is_available():
     device = "cuda"

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -49,8 +49,8 @@ def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
     os.makedirs(bert_dir, exist_ok=True)
     if torch.cuda.is_available():
         device = "cuda:0"
-    elif torch.backends.mps.is_available():
-        device = "mps"
+    # elif torch.backends.mps.is_available():
+    #     device = "mps"
     else:
         device = "cpu"
     tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)

GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py

@@ -50,8 +50,8 @@ def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
 alpha=0.5
 if torch.cuda.is_available():
     device = "cuda:0"
-elif torch.backends.mps.is_available():
-    device = "mps"
+# elif torch.backends.mps.is_available():
+#     device = "mps"
 else:
     device = "cpu"
 model=cnhubert.get_model()

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -40,8 +40,8 @@
 
     if torch.cuda.is_available():
         device = "cuda"
-    elif torch.backends.mps.is_available():
-        device = "mps"
+    # elif torch.backends.mps.is_available():
+    #     device = "mps"
     else:
         device = "cpu"
     hps = utils.get_hparams_from_file(s2config_path)

GPT_SoVITS/s1_train.py

@@ -118,16 +118,16 @@ def main(args):
     os.environ["MASTER_ADDR"]="localhost"
     trainer: Trainer = Trainer(
         max_epochs=config["train"]["epochs"],
-        accelerator="gpu",
+        accelerator="gpu" if torch.cuda.is_available() else "cpu",
         # val_check_interval=9999999999999999999999,###不要验证
         # check_val_every_n_epoch=None,
         limit_val_batches=0,
-        devices=-1,
+        devices=-1 if torch.cuda.is_available() else 1,
         benchmark=False,
         fast_dev_run=False,
-        strategy = "auto" if torch.backends.mps.is_available() else DDPStrategy(
+        strategy = DDPStrategy(
             process_group_backend="nccl" if platform.system() != "Windows" else "gloo"
-        ),  # mps 不支持多节点训练
+        ) if torch.cuda.is_available() else "auto",
         precision=config["train"]["precision"],
         logger=logger,
         num_sanity_val_steps=0,

GPT_SoVITS/s2_train.py

@@ -41,15 +41,15 @@
 # from config import pretrained_s2G,pretrained_s2D
 global_step = 0
 
+device = "cpu"  # cuda以外的设备，等mps优化后加入
+
 
 def main():
-    """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:
+    if torch.cuda.is_available():
         n_gpus = torch.cuda.device_count()
+    else:
+        n_gpus = 1
     os.environ["MASTER_ADDR"] = "localhost"
     os.environ["MASTER_PORT"] = str(randint(20000, 55555))
 
@@ -73,7 +73,7 @@ def run(rank, n_gpus, hps):
         writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
 
     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 not torch.cuda.is_available() else "nccl",
         init_method="env://",
         world_size=n_gpus,
         rank=rank,
@@ -137,9 +137,9 @@ def run(rank, n_gpus, hps):
         hps.train.segment_size // hps.data.hop_length,
         n_speakers=hps.data.n_speakers,
         **hps.model,
-    ).to("mps")
+    ).to(device)
 
-    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(device)
     for name, param in net_g.named_parameters():
         if not param.requires_grad:
             print(name, "not requires_grad")
@@ -187,8 +187,8 @@ def run(rank, n_gpus, hps):
         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("mps")
-        net_d = net_d.to("mps")
+        net_g = net_g.to(device)
+        net_d = net_d.to(device)
 
     try:  # 如果能加载自动resume
         _, _, _, epoch_str = utils.load_checkpoint(
@@ -320,12 +320,12 @@ def train_and_evaluate(
                 rank, non_blocking=True
             )
         else:
-            spec, spec_lengths = spec.to("mps"), spec_lengths.to("mps")
-            y, y_lengths = y.to("mps"), y_lengths.to("mps")
-            ssl = ssl.to("mps")
+            spec, spec_lengths = spec.to(device), spec_lengths.to(device)
+            y, y_lengths = y.to(device), y_lengths.to(device)
+            ssl = ssl.to(device)
             ssl.requires_grad = False
             # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = text.to("mps"), text_lengths.to("mps")
+            text, text_lengths = text.to(device), text_lengths.to(device)
 
         with autocast(enabled=hps.train.fp16_run):
             (
@@ -532,10 +532,10 @@ def evaluate(hps, generator, eval_loader, writer_eval):
                 ssl = ssl.cuda()
                 text, text_lengths = text.cuda(), text_lengths.cuda()
             else:
-                spec, spec_lengths = spec.to("mps"), spec_lengths.to("mps")
-                y, y_lengths = y.to("mps"), y_lengths.to("mps")
-                ssl = ssl.to("mps")
-                text, text_lengths = text.to("mps"), text_lengths.to("mps")
+                spec, spec_lengths = spec.to(device), spec_lengths.to(device)
+                y, y_lengths = y.to(device), y_lengths.to(device)
+                ssl = ssl.to(device)
+                text, text_lengths = text.to(device), text_lengths.to(device)
             for test in [0, 1]:
                 y_hat, mask, *_ = generator.module.infer(
                     ssl, spec, spec_lengths, text, text_lengths, test=test

api.py

@@ -13,7 +13,7 @@
 `-dt` - `默认参考音频文本`
 `-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
 
-`-d` - `推理设备, "cuda","cpu","mps"`
+`-d` - `推理设备, "cuda","cpu"`
 `-a` - `绑定地址, 默认"127.0.0.1"`
 `-p` - `绑定端口, 默认9880, 可在 config.py 中指定`
 `-fp` - `覆盖 config.py 使用全精度`
@@ -143,7 +143,7 @@
 parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
 parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
 
-parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu / mps")
+parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
 parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
 parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
 parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
@@ -482,9 +482,6 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
     wav.seek(0)
 
     torch.cuda.empty_cache()
-    if device == "mps":
-        print('executed torch.mps.empty_cache()')
-        torch.mps.empty_cache()
     return StreamingResponse(wav, media_type="audio/wav")
 
 

webui.py

@@ -55,7 +55,7 @@
 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
+# 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()
 
@@ -73,18 +73,19 @@
             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
+# # 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
 else:
-    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
-    default_batch_size = 1
+    gpu_info = ("%s\t%s" % ("0", "CPU"))
+    gpu_infos.append("%s\t%s" % ("0", "CPU"))
+    default_batch_size = psutil.virtual_memory().total/ 1024 / 1024 / 1024 / 2
 gpus = "-".join([i[0] for i in gpu_infos])
 
 pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth"