import argparse import os import sys import json import shutil from multiprocessing import cpu_count import torch import logging from model import device, fp16 logger = logging.getLogger(__name__) version_config_list = [ "v1/32k.json", "v1/40k.json", "v1/48k.json", "v2/48k.json", "v2/32k.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 = str(device) self.is_half = fp16 self.use_jit = False self.n_cpu = 0 self.gpu_name = None self.json_config = self.load_config_json() self.gpu_mem = None ( self.python_cmd, self.listen_port, self.iscolab, self.noparallel, self.noautoopen, self.dml, ) = self.arg_parse() self.instead = "" self.preprocess_per = 3.7 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: # p = f"configs/inuse/{config_file}" # if not os.path.exists(p): # shutil.copy(f"configs/{config_file}", p) # with open(f"configs/inuse/{config_file}", "r") as f: # d[config_file] = json.load(f) return d @staticmethod def arg_parse() -> tuple: exe = sys.executable or "python" parser = argparse.ArgumentParser() parser.add_argument("--port", type=int, default=7865, help="Listen port") parser.add_argument("--pycmd", type=str, default=exe, help="Python command") parser.add_argument("--colab", action="store_true", help="Launch in colab") parser.add_argument( "--noparallel", action="store_true", help="Disable parallel processing" ) parser.add_argument( "--noautoopen", action="store_true", help="Do not open in browser automatically", ) parser.add_argument( "--dml", action="store_true", help="torch_dml", ) cmd_opts = parser.parse_args() cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865 return ( cmd_opts.pycmd, cmd_opts.port, cmd_opts.colab, cmd_opts.noparallel, cmd_opts.noautoopen, cmd_opts.dml, ) # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+. # check `getattr` and try it for compatibility @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"configs/inuse/{config_file}", "r") as f: strr = f.read().replace("true", "false") with open(f"configs/inuse/{config_file}", "w") as f: f.write(strr) logger.info("overwrite " + config_file) self.preprocess_per = 3.0 logger.info("overwrite preprocess_per to %d" % (self.preprocess_per)) 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(0) 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 ): logger.info("Found GPU %s, force to fp32", self.gpu_name) self.is_half = False self.use_fp32_config() else: logger.info("Found GPU %s", self.gpu_name) self.gpu_mem = int( torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024 + 0.4 ) if self.gpu_mem <= 4: self.preprocess_per = 3.0 elif self.has_mps(): logger.info("No supported Nvidia GPU found") self.device = self.instead = "mps" self.is_half = False self.use_fp32_config() else: logger.info("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: # 6G显存配置 x_pad = 3 x_query = 10 x_center = 60 x_max = 65 else: # 5G显存配置 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 if self.dml: logger.info("Use DirectML instead") if ( os.path.exists( "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll" ) == False ): try: os.rename( "runtime\Lib\site-packages\onnxruntime", "runtime\Lib\site-packages\onnxruntime-cuda", ) except: pass try: os.rename( "runtime\Lib\site-packages\onnxruntime-dml", "runtime\Lib\site-packages\onnxruntime", ) except: pass # if self.device != "cpu": import torch_directml self.device = torch_directml.device(torch_directml.default_device()) self.is_half = False else: if self.instead: logger.info(f"Use {self.instead} instead") if ( os.path.exists( "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll" ) == False ): try: os.rename( "runtime\Lib\site-packages\onnxruntime", "runtime\Lib\site-packages\onnxruntime-dml", ) except: pass try: os.rename( "runtime\Lib\site-packages\onnxruntime-cuda", "runtime\Lib\site-packages\onnxruntime", ) except: pass logger.info( "Half-precision floating-point: %s, device: %s" % (self.is_half, self.device) ) return x_pad, x_query, x_center, x_max