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ipex cleanup

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Disty0
2023-07-28 01:35:02 +03:00
parent 1113583c7c
commit 38dcca7399
1 changed files with 23 additions and 26 deletions
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modules/devices.py
+23 -26
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@@ -221,21 +221,21 @@ if backend == 'ipex':
#Functions with dtype errors:
CondFunc('torch.nn.modules.GroupNorm.forward',
lambda orig_func, *args, **kwargs: orig_func(args[0], args[1].to(args[0].weight.data.dtype)),
lambda *args, **kwargs: args[2].dtype != args[1].weight.data.dtype)
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)
CondFunc('torch.nn.modules.Linear.forward',
lambda orig_func, *args, **kwargs: orig_func(args[0], args[1].to(args[0].weight.data.dtype)),
lambda *args, **kwargs: args[2].dtype != args[1].weight.data.dtype)
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)
#Diffusers bfloat16:
CondFunc('torch.nn.modules.Conv2d._conv_forward',
lambda orig_func, *args, **kwargs: orig_func(args[0], args[1].to(args[2].data.dtype), args[2], args[3]),
lambda *args, **kwargs: args[2].dtype != args[3].data.dtype)
lambda orig_func, self, input, weight, bias=None: orig_func(self, input.to(weight.data.dtype), weight, bias=bias),
lambda orig_func, self, input, weight, bias=None: input.dtype != weight.data.dtype)
#Functions that does not work with the XPU:
#UniPC:
CondFunc('torch.linalg.solve',
lambda orig_func, *args, **kwargs: orig_func(args[0].to("cpu"), args[1].to("cpu")).to(get_cuda_device_string()),
lambda *args, **kwargs: args[1].device != torch.device("cpu"))
lambda orig_func, A, B, *args, left=True, out=None: orig_func(A.to("cpu"), B.to("cpu"), *args, left=left, out=out).to(get_cuda_device_string()),
lambda orig_func, A, B, *args, left=True, out=None: A.device != torch.device("cpu") or B.device != torch.device("cpu"))
#SDE Samplers:
CondFunc('torch.Generator',
lambda orig_func, device: torch.xpu.Generator(device),
@@ -247,25 +247,22 @@ if backend == 'ipex':
#Diffusers Float64 (ARC GPUs doesn't support double or Float64):
if not torch.xpu.has_fp64_dtype():
CondFunc('torch.from_numpy',
lambda orig_func, *args, **kwargs: orig_func(args[0].astype('float32')),
lambda *args, **kwargs: args[1].dtype == float)
#ControlNet:
CondFunc('torch.instance_norm',
lambda orig_func, *args, **kwargs: orig_func(args[0].to("cpu"),
args[1].to("cpu") if args[1] is not None else args[1],
args[2].to("cpu") if args[2] is not None else args[2],
args[3].to("cpu") if args[3] is not None else args[3],
args[4].to("cpu") if args[4] is not None else args[4],
args[5], args[6], args[7], args[8]).to(get_cuda_device_string()),
lambda *args, **kwargs: args[1].device != torch.device("cpu"))
#Tiled VAE:
#Create xpu tensors for weight (ones) and bias (zeros) if they're not provided
lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
lambda orig_func, ndarray: ndarray.dtype == float)
#ControlNet and TiledVAE:
CondFunc('torch.batch_norm',
lambda orig_func, *args, **kwargs: orig_func(args[0],
args[1] or torch.ones(args[0].size()[1], device=get_cuda_device_string()),
args[2] or torch.zeros(args[0].size()[1], device=get_cuda_device_string()),
args[3], args[4], args[5], args[6], args[7], args[8]),
lambda orig_func, *args, **kwargs: args[0].device != torch.device("cpu"))
lambda orig_func, input, running_mean=None, running_var=None, weight=None, bias=None, training=False, momentum=0.1, eps=1e-5, cudnn_enabled=True: orig_func(input,
running_mean if running_mean is not None else torch.ones(input.size()[1], device=get_cuda_device_string()),
running_var if running_var is not None else torch.zeros(input.size()[1], device=get_cuda_device_string()),
weight, bias, training, momentum, eps, cudnn_enabled),
lambda orig_func, input, running_mean=None, running_var=None, weight=None, bias=None, training=False, momentum=0.1, eps=1e-5, cudnn_enabled=True: input.device != torch.device("cpu"))
#ControlNet
CondFunc('torch.instance_norm',
lambda orig_func, input, running_mean=None, running_var=None, weight=None, bias=None, use_input_stats=True, momentum=0.1, eps=1e-5, cudnn_enabled=True: orig_func(input,
running_mean if running_mean is not None else torch.ones(input.size()[1], device=get_cuda_device_string()),
running_var if running_var is not None else torch.zeros(input.size()[1], device=get_cuda_device_string()),
weight, bias, use_input_stats, momentum, eps, cudnn_enabled),
lambda orig_func, input, running_mean=None, running_var=None, weight=None, bias=None, use_input_stats=True, momentum=0.1, eps=1e-5, cudnn_enabled=True: input.device != torch.device("cpu"))
if backend == "directml":
directml_init()