IPEX Diffusers remove BF16 fixes

modules/intel/ipex/hijacks.py

@@ -19,14 +19,6 @@ def ipex_autocast(*args, **kwargs):
     else:
         return original_autocast(*args, **kwargs)
 
-#Diffusers BF16:
-original_linear_forward = torch.nn.modules.Linear.forward
-def linear_forward(self, input):
-    if input.dtype != self.weight.data.dtype:
-        return original_linear_forward(self, input.to(self.weight.data.dtype))
-    else:
-        return original_linear_forward(self, input)
-
 #Embedding BF16
 original_torch_cat = torch.cat
 def torch_cat(input, *args, **kwargs):
@@ -35,14 +27,6 @@ def torch_cat(input, *args, **kwargs):
     else:
         return original_torch_cat(input, *args, **kwargs)
 
-original_conv2d = torch.nn.functional.conv2d
-#Diffusers BF16:
-def conv2d(input, weight, *args, **kwargs):
-    if input.dtype != weight.data.dtype:
-        return original_conv2d(input.to(weight.data.dtype), weight, *args, **kwargs)
-    else:
-        return original_conv2d(input, weight, *args, **kwargs)
-
 original_interpolate = torch.nn.functional.interpolate
 #Latent antialias:
 def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False):
@@ -101,7 +85,7 @@ def ipex_hijacks():
         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:
-        input.dtype != weight.data.dtype and weight is not None)
+        weight is not None and input.dtype != weight.data.dtype)
 
     #Functions that does not work with the XPU:
     #UniPC:
@@ -132,7 +116,5 @@ def ipex_hijacks():
 
     #Functions that make compile mad with CondFunc:
     torch.autocast = ipex_autocast
-    torch.nn.modules.Linear.forward = linear_forward
     torch.cat = torch_cat
-    torch.nn.functional.conv2d = conv2d
     torch.nn.functional.interpolate = interpolate