import torch import json import os version_config_list = [ "v1/32000.json", "v1/40000.json", "v1/48000.json", "v2/48000.json", "v2/32000.json", ] def singleton_variable(func): def wrapper(*args, **kwargs): if not wrapper.instance: wrapper.instance = func(*args, **kwargs) return wrapper.instance wrapper.instance = None return wrapper @singleton_variable class Config: def __init__(self): self.device = "cuda:0" self.is_half = True self.use_jit = False self.n_cpu = 0 self.gpu_name = None self.json_config = self.load_config_json() self.gpu_mem = None self.instead = "" self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() @staticmethod def load_config_json() -> dict: d = {} for config_file in version_config_list: with open(f"rvc/configs/{config_file}", "r") as f: d[config_file] = json.load(f) return d @staticmethod def has_mps() -> bool: if not torch.backends.mps.is_available(): return False try: torch.zeros(1).to(torch.device("mps")) return True except Exception: return False @staticmethod def has_xpu() -> bool: if hasattr(torch, "xpu") and torch.xpu.is_available(): return True else: return False def use_fp32_config(self): print( f"Using FP32 config instead of FP16 due to GPU compatibility ({self.gpu_name})" ) for config_file in version_config_list: self.json_config[config_file]["train"]["fp16_run"] = False with open(f"rvc/configs/{config_file}", "r") as f: strr = f.read().replace("true", "false") with open(f"rvc/configs/{config_file}", "w") as f: f.write(strr) with open("rvc/train/preprocess/preprocess.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open("rvc/train/preprocess/preprocess.py", "w") as f: f.write(strr) def device_config(self) -> tuple: if torch.cuda.is_available(): if self.has_xpu(): self.device = self.instead = "xpu:0" self.is_half = True i_device = int(self.device.split(":")[-1]) self.gpu_name = torch.cuda.get_device_name(i_device) if ( ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) or "P40" in self.gpu_name.upper() or "P10" in self.gpu_name.upper() or "1060" in self.gpu_name or "1070" in self.gpu_name or "1080" in self.gpu_name ): self.is_half = False self.use_fp32_config() self.gpu_mem = int( torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024 + 0.4 ) if self.gpu_mem <= 4: with open("rvc/train/preprocess/preprocess.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open("rvc/train/preprocess/preprocess.py", "w") as f: f.write(strr) elif self.has_mps(): print("No supported Nvidia GPU found") self.device = self.instead = "mps" self.is_half = False self.use_fp32_config() else: print("No supported Nvidia GPU found") self.device = self.instead = "cpu" self.is_half = False self.use_fp32_config() if self.n_cpu == 0: self.n_cpu = os.cpu_count() if self.is_half: x_pad = 3 x_query = 10 x_center = 60 x_max = 65 else: x_pad = 1 x_query = 6 x_center = 38 x_max = 41 if self.gpu_mem is not None and self.gpu_mem <= 4: x_pad = 1 x_query = 5 x_center = 30 x_max = 32 return x_pad, x_query, x_center, x_max def max_vram_gpu(gpu): if torch.cuda.is_available(): gpu_properties = torch.cuda.get_device_properties(gpu) total_memory_gb = round(gpu_properties.total_memory / 1024 / 1024 / 1024) return total_memory_gb else: return "0" def get_gpu_info(): ngpu = torch.cuda.device_count() gpu_infos = [] if torch.cuda.is_available() or ngpu != 0: for i in range(ngpu): gpu_name = torch.cuda.get_device_name(i) mem = int( torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 + 0.4 ) gpu_infos.append("%s: %s %s GB" % (i, gpu_name, mem)) if len(gpu_infos) > 0: gpu_info = "\n".join(gpu_infos) else: gpu_info = "Unfortunately, there is no compatible GPU available to support your training." return gpu_info