Disable IPEX attention if the GPU supports 64 bit

library/ipex/__init__.py

@@ -165,6 +165,7 @@ def ipex_init(): # pylint: disable=too-many-statements
             torch.cuda.get_device_id_list_per_card = torch.xpu.get_device_id_list_per_card
 
         ipex_hijacks()
+        if not torch.xpu.has_fp64_dtype():
             attention_init()
             try:
                 from .diffusers import ipex_diffusers

library/ipex/diffusers.py

@@ -1,6 +1,6 @@
 import torch
 import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-import diffusers #0.21.1 # pylint: disable=import-error
+import diffusers #0.24.0 # pylint: disable=import-error
 from diffusers.models.attention_processor import Attention
 
 # pylint: disable=protected-access, missing-function-docstring, line-too-long

library/ipex/gradscaler.py

@@ -5,6 +5,7 @@ import intel_extension_for_pytorch._C as core # pylint: disable=import-error, un
 
 # pylint: disable=protected-access, missing-function-docstring, line-too-long
 
+device_supports_fp64 = torch.xpu.has_fp64_dtype()
 OptState = ipex.cpu.autocast._grad_scaler.OptState
 _MultiDeviceReplicator = ipex.cpu.autocast._grad_scaler._MultiDeviceReplicator
 _refresh_per_optimizer_state = ipex.cpu.autocast._grad_scaler._refresh_per_optimizer_state
@@ -96,6 +97,9 @@ def unscale_(self, optimizer):
 
     # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
     assert self._scale is not None
+    if device_supports_fp64:
+        inv_scale = self._scale.double().reciprocal().float()
+    else:
         inv_scale = self._scale.to("cpu").double().reciprocal().float().to(self._scale.device)
     found_inf = torch.full(
         (1,), 0.0, dtype=torch.float32, device=self._scale.device

library/ipex/hijacks.py

@@ -89,7 +89,7 @@ def ipex_autocast(*args, **kwargs):
     else:
         return original_autocast(*args, **kwargs)
 
-#Embedding BF16
+# Embedding BF16
 original_torch_cat = torch.cat
 def torch_cat(tensor, *args, **kwargs):
     if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype):
@@ -97,7 +97,7 @@ def torch_cat(tensor, *args, **kwargs):
     else:
         return original_torch_cat(tensor, *args, **kwargs)
 
-#Latent antialias:
+# Latent antialias:
 original_interpolate = torch.nn.functional.interpolate
 def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
     if antialias or align_corners is not None:
@@ -160,7 +160,7 @@ def ipex_hijacks():
         lambda orig_func, device=None: torch.xpu.Generator(return_xpu(device)),
         lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
 
-    #TiledVAE and ControlNet:
+    # TiledVAE and ControlNet:
     CondFunc('torch.batch_norm',
         lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
         weight if weight is not None else torch.ones(input.size()[1], device=input.device),
@@ -172,41 +172,41 @@ def ipex_hijacks():
         bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
         lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
 
-    #Functions with dtype errors:
+    # Functions with dtype errors:
     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)
-    #Training:
+    # Training:
     CondFunc('torch.nn.modules.linear.Linear.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)
     CondFunc('torch.nn.modules.conv.Conv2d.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)
-    #BF16:
+    # BF16:
     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)
-    #SwinIR BF16:
+    # SwinIR BF16:
     CondFunc('torch.nn.functional.pad',
         lambda orig_func, input, pad, mode='constant', value=None: orig_func(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16),
         lambda orig_func, input, pad, mode='constant', value=None: mode == 'reflect' and input.dtype == torch.bfloat16)
 
-    #Diffusers Float64 (ARC GPUs doesn't support double or Float64):
+    # Diffusers Float64 (Alchemist GPUs doesn't support 64 bit):
     if not torch.xpu.has_fp64_dtype():
         CondFunc('torch.from_numpy',
         lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
         lambda orig_func, ndarray: ndarray.dtype == float)
 
-    #Broken functions when torch.cuda.is_available is True:
-    #Pin Memory:
+    # Broken functions when torch.cuda.is_available is True:
+    # Pin Memory:
     CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
         lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
         lambda orig_func, *args, **kwargs: True)
 
-    #Functions that make compile mad with CondFunc:
+    # Functions that make compile mad with CondFunc:
     torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
     torch.nn.DataParallel = DummyDataParallel
     torch.autocast = ipex_autocast