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