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import argparse
import os
import sys
import json
from multiprocessing import cpu_count
import torch
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):
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 = 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
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