File size: 5,130 Bytes
85d3b29 1a7d583 85d3b29 1a7d583 85d3b29 1a7d583 85d3b29 889c346 85d3b29 889c346 85d3b29 889c346 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
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
|