255 lines
7.8 KiB
Python
255 lines
7.8 KiB
Python
import argparse
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import os
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import sys
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import json
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import shutil
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from multiprocessing import cpu_count
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import torch
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try:
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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if torch.xpu.is_available():
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from infer.modules.ipex import ipex_init
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ipex_init()
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except Exception: # pylint: disable=broad-exception-caught
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pass
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import logging
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logger = logging.getLogger(__name__)
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version_config_list = [
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"v1/32k.json",
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"v1/40k.json",
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"v1/48k.json",
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"v2/48k.json",
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"v2/32k.json",
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]
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def singleton_variable(func):
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def wrapper(*args, **kwargs):
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if not wrapper.instance:
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wrapper.instance = func(*args, **kwargs)
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return wrapper.instance
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wrapper.instance = None
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return wrapper
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@singleton_variable
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class Config:
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def __init__(self):
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self.device = "cuda:0"
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self.is_half = True
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self.use_jit = False
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self.n_cpu = 0
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self.gpu_name = None
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self.json_config = self.load_config_json()
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self.gpu_mem = None
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(
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self.python_cmd,
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self.listen_port,
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self.iscolab,
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self.noparallel,
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self.noautoopen,
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self.dml,
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) = self.arg_parse()
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self.instead = ""
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self.preprocess_per = 3.7
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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@staticmethod
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def load_config_json() -> dict:
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d = {}
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for config_file in version_config_list:
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p = f"configs/inuse/{config_file}"
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if not os.path.exists(p):
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shutil.copy(f"configs/{config_file}", p)
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with open(f"configs/inuse/{config_file}", "r") as f:
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d[config_file] = json.load(f)
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return d
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@staticmethod
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def arg_parse() -> tuple:
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exe = sys.executable or "python"
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parser = argparse.ArgumentParser()
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parser.add_argument("--port", type=int, default=7865, help="Listen port")
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parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
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parser.add_argument("--colab", action="store_true", help="Launch in colab")
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parser.add_argument(
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"--noparallel", action="store_true", help="Disable parallel processing"
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)
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parser.add_argument(
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"--noautoopen",
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action="store_true",
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help="Do not open in browser automatically",
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)
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parser.add_argument(
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"--dml",
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action="store_true",
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help="torch_dml",
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)
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cmd_opts = parser.parse_args()
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cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
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return (
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cmd_opts.pycmd,
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cmd_opts.port,
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cmd_opts.colab,
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cmd_opts.noparallel,
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cmd_opts.noautoopen,
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cmd_opts.dml,
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)
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# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
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# check `getattr` and try it for compatibility
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@staticmethod
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def has_mps() -> bool:
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if not torch.backends.mps.is_available():
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return False
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try:
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torch.zeros(1).to(torch.device("mps"))
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return True
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except Exception:
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return False
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@staticmethod
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def has_xpu() -> bool:
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if hasattr(torch, "xpu") and torch.xpu.is_available():
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return True
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else:
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return False
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def use_fp32_config(self):
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for config_file in version_config_list:
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self.json_config[config_file]["train"]["fp16_run"] = False
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with open(f"configs/inuse/{config_file}", "r") as f:
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strr = f.read().replace("true", "false")
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with open(f"configs/inuse/{config_file}", "w") as f:
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f.write(strr)
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logger.info("overwrite " + config_file)
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self.preprocess_per = 3.0
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logger.info("overwrite preprocess_per to %d" % (self.preprocess_per))
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def device_config(self) -> tuple:
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if torch.cuda.is_available():
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if self.has_xpu():
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self.device = self.instead = "xpu:0"
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self.is_half = True
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i_device = int(self.device.split(":")[-1])
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self.gpu_name = torch.cuda.get_device_name(i_device)
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if (
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
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or "P40" in self.gpu_name.upper()
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or "P10" in self.gpu_name.upper()
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or "1060" in self.gpu_name
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or "1070" in self.gpu_name
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or "1080" in self.gpu_name
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):
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logger.info("Found GPU %s, force to fp32", self.gpu_name)
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self.is_half = False
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self.use_fp32_config()
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else:
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logger.info("Found GPU %s", self.gpu_name)
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self.gpu_mem = int(
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torch.cuda.get_device_properties(i_device).total_memory
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/ 1024
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/ 1024
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/ 1024
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+ 0.4
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)
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if self.gpu_mem <= 4:
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self.preprocess_per = 3.0
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elif self.has_mps():
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logger.info("No supported Nvidia GPU found")
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self.device = self.instead = "mps"
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self.is_half = False
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self.use_fp32_config()
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else:
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logger.info("No supported Nvidia GPU found")
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self.device = self.instead = "cpu"
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self.is_half = False
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self.use_fp32_config()
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if self.n_cpu == 0:
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self.n_cpu = cpu_count()
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if self.is_half:
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# 6G显存配置
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x_pad = 3
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x_query = 10
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x_center = 60
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x_max = 65
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else:
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# 5G显存配置
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x_pad = 1
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x_query = 6
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x_center = 38
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x_max = 41
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if self.gpu_mem is not None and self.gpu_mem <= 4:
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32
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if self.dml:
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logger.info("Use DirectML instead")
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if (
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os.path.exists(
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"runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
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)
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== False
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):
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try:
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os.rename(
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"runtime\Lib\site-packages\onnxruntime",
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"runtime\Lib\site-packages\onnxruntime-cuda",
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)
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except:
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pass
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try:
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os.rename(
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"runtime\Lib\site-packages\onnxruntime-dml",
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"runtime\Lib\site-packages\onnxruntime",
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)
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except:
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pass
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# if self.device != "cpu":
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import torch_directml
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self.device = torch_directml.device(torch_directml.default_device())
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self.is_half = False
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else:
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if self.instead:
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logger.info(f"Use {self.instead} instead")
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if (
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os.path.exists(
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"runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
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)
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== False
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):
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try:
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os.rename(
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"runtime\Lib\site-packages\onnxruntime",
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"runtime\Lib\site-packages\onnxruntime-dml",
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)
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except:
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pass
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try:
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os.rename(
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"runtime\Lib\site-packages\onnxruntime-cuda",
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"runtime\Lib\site-packages\onnxruntime",
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)
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except:
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pass
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logger.info(
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"Half-precision floating-point: %s, device: %s"
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% (self.is_half, self.device)
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)
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return x_pad, x_query, x_center, x_max
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