mirror of
https://github.com/vladmandic/automatic
synced 2026-09-04 03:50:44 +02:00
Disty0
121 lines
6.9 KiB
Python
121 lines
6.9 KiB
Python
import torch
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import intel_extension_for_pytorch as ipex
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from modules import devices
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from modules.sd_hijack_utils import CondFunc
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def ipex_no_cuda(orig_func, *args, **kwargs): # pylint: disable=redefined-outer-name
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torch.cuda.is_available = lambda: False
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orig_func(*args, **kwargs)
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torch.cuda.is_available = torch.xpu.is_available
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#Autocast
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original_autocast = torch.autocast
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def ipex_autocast(*args, **kwargs):
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if args[0] == "cuda" or args[0] == "xpu":
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if "dtype" in kwargs:
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return original_autocast("xpu", *args[1:], **kwargs)
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else:
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return original_autocast("xpu", *args[1:], dtype=devices.dtype, **kwargs)
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else:
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return original_autocast(*args, **kwargs)
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#Embedding BF16
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original_torch_cat = torch.cat
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def torch_cat(input, *args, **kwargs):
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if len(input) == 3 and (input[0].dtype != input[1].dtype or input[2].dtype != input[1].dtype):
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return original_torch_cat([input[0].to(input[1].dtype), input[1], input[2].to(input[1].dtype)], *args, **kwargs)
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else:
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return original_torch_cat(input, *args, **kwargs)
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original_interpolate = torch.nn.functional.interpolate
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#Latent antialias:
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def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False):
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if antialias:
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return original_interpolate(input.to("cpu"), size=size, scale_factor=scale_factor, mode=mode,
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align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(devices.device)
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else:
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return original_interpolate(input, size=size, scale_factor=scale_factor, mode=mode,
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align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias)
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def ipex_hijacks():
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#Libraries that blindly uses cuda:
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#Adetailer:
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CondFunc('torch.Tensor.to',
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lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, devices.device, *args, **kwargs),
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lambda orig_func, self, device=None, *args, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
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CondFunc('torch.empty',
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lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=devices.device, **kwargs),
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lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
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#ControlNet depth_leres
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CondFunc('torch.load',
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lambda orig_func, *args, map_location=None, **kwargs: orig_func(*args, devices.device, **kwargs),
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lambda orig_func, *args, map_location=None, **kwargs: (map_location is None) or (type(map_location) is torch.device and map_location.type == "cuda") or (type(map_location) is str and "cuda" in map_location))
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#Diffusers Model CPU Offload:
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CondFunc('torch.randn',
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lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=devices.device, **kwargs),
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lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
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#Other:
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CondFunc('torch.ones',
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lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=devices.device, **kwargs),
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lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
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CondFunc('torch.zeros',
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lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=devices.device, **kwargs),
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lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
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CondFunc('torch.tensor',
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lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=devices.device, **kwargs),
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lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
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#Broken functions when torch.cuda.is_available is True:
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#Pin Memory:
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CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
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lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
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lambda orig_func, *args, **kwargs: True)
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#Functions with dtype errors:
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#Original backend:
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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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#Embedding FP32:
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CondFunc('torch.bmm',
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lambda orig_func, input, mat2, *args, **kwargs: orig_func(input, mat2.to(input.dtype), *args, **kwargs),
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lambda orig_func, input, mat2, *args, **kwargs: input.dtype != mat2.dtype)
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#BF16:
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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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#Functions that does not work with the XPU:
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#UniPC:
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CondFunc('torch.linalg.solve',
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lambda orig_func, A, B, *args, **kwargs: orig_func(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(devices.device),
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lambda orig_func, A, B, *args, **kwargs: A.device != torch.device("cpu") or B.device != torch.device("cpu"))
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#SDE Samplers:
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CondFunc('torch.Generator',
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lambda orig_func, device: torch.xpu.Generator(device),
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lambda orig_func, device: device != torch.device("cpu") and device != "cpu")
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#Diffusers Float64 (ARC GPUs doesn't support double or Float64):
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if not torch.xpu.has_fp64_dtype():
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CondFunc('torch.from_numpy',
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lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
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lambda orig_func, ndarray: ndarray.dtype == float)
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#ControlNet and TiledVAE:
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CondFunc('torch.batch_norm',
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lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
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weight if weight is not None else torch.ones(input.size()[1], device=devices.device),
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bias if bias is not None else torch.zeros(input.size()[1], device=devices.device), *args, **kwargs),
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lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
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#ControlNet
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CondFunc('torch.instance_norm',
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lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
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weight if weight is not None else torch.ones(input.size()[1], device=devices.device),
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bias if bias is not None else torch.zeros(input.size()[1], device=devices.device), *args, **kwargs),
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lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
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#Functions that make compile mad with CondFunc:
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torch.autocast = ipex_autocast
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torch.cat = torch_cat
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torch.nn.functional.interpolate = interpolate
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