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import contextlib
from modules import shared
from modules.sd_hijack_utils import CondFunc
has_ipex = False
try:
import torch
import intel_extension_for_pytorch as ipex
has_ipex = True
except Exception:
pass
def check_for_xpu():
if not has_ipex:
return False
return hasattr(torch, 'xpu') and torch.xpu.is_available()
has_xpu = check_for_xpu()
def get_xpu_device_string():
if shared.cmd_opts.device_id is not None:
return f"xpu:{shared.cmd_opts.device_id}"
return "xpu"
def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
return contextlib.nullcontext()
if has_xpu:
CondFunc('torch.Generator',
lambda orig_func, device=None: torch.xpu.Generator(device),
lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
CondFunc('torch.nn.functional.layer_norm',
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
weight is not None and input.dtype != weight.data.dtype)
CondFunc('torch.nn.modules.GroupNorm.forward',
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
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