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Merge pull request #3802 from vladmandic/dev

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Dev
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Disty0
2025-03-06 21:40:42 +03:00
committed by GitHub
parent 431bcc3609 944d1b15c5
commit 9ea16a301a
3 changed files with 67 additions and 42 deletions
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CHANGELOG.md
+5
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@@ -1,5 +1,10 @@
# Change Log for SD.Next
## Update for 2025-03-06
- fix installer not starting when older version of rich is installed
- ipex, fix untyped_storage and torch.eye
## Update for 2025-02-28
Primarily a hotfix/service release plus few UI improvements and one exciting new feature: Remote-VAE!
installer.py
-1
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@@ -91,7 +91,6 @@ def install_traceback(suppress: list = []):
extra_lines=os.environ.get('SD_TRACELINES', 1),
max_frames=os.environ.get('SD_TRACEFRAMES', 16),
width=os.environ.get('SD_TRACEWIDTH', console.width),
code_width=os.environ.get('SD_TRACEWIDTH', console.width) - 12,
word_wrap=os.environ.get('SD_TRACEWRAP', False),
indent_guides=os.environ.get('SD_TRACEINDENT', False),
show_locals=os.environ.get('SD_TRACELOCALS', False),
modules/intel/ipex/hijacks.py
+62 -41
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@@ -5,10 +5,10 @@ import torch
import numpy as np
from modules import devices, errors
device_supports_fp64 = torch.xpu.has_fp64_dtype() if hasattr(torch.xpu, "has_fp64_dtype") else torch.xpu.get_device_properties("xpu").has_fp64
if os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '0' and (torch.xpu.get_device_properties("xpu").total_memory / 1024 / 1024 / 1024) > 4.1:
device_supports_fp64 = torch.xpu.has_fp64_dtype() if hasattr(torch.xpu, "has_fp64_dtype") else torch.xpu.get_device_properties(devices.device).has_fp64
if os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '0' and (torch.xpu.get_device_properties(devices.device).total_memory / 1024 / 1024 / 1024) > 4.1:
try:
x = torch.ones((33000,33000), dtype=torch.float32, device="xpu")
x = torch.ones((33000,33000), dtype=torch.float32, device=devices.device)
del x
torch.xpu.empty_cache()
can_allocate_plus_4gb = True
@@ -30,13 +30,19 @@ def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
@property
def is_cuda(self):
return self.device.type == 'xpu' or self.device.type == 'cuda'
return self.device.type == "xpu" or self.device.type == "cuda"
def check_device(device):
return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int))
def check_device_type(device, device_type: str) -> bool:
if device is None or type(device) not in {str, int, torch.device}:
return False
else:
return bool(torch.device(device).type == device_type)
def return_xpu(device):
return f"xpu:{device.split(':')[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device(f"xpu:{device.index}" if device.index is not None else "xpu") if isinstance(device, torch.device) else "xpu"
def check_cuda(device) -> bool:
return bool(isinstance(device, int) or check_device_type(device, "cuda"))
def return_xpu(device): # keep the device instance type, aka return string if the input is string
return devices.device if device is None else f"xpu:{device.split(':')[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device(f"xpu:{device.index}" if device.index is not None else "xpu") if isinstance(device, torch.device) else "xpu"
# Autocast
@@ -69,17 +75,16 @@ original_from_numpy = torch.from_numpy
@wraps(torch.from_numpy)
def from_numpy(ndarray):
if ndarray.dtype == float:
return original_from_numpy(ndarray.astype('float32'))
return original_from_numpy(ndarray.astype("float32"))
else:
return original_from_numpy(ndarray)
original_as_tensor = torch.as_tensor
@wraps(torch.as_tensor)
def as_tensor(data, dtype=None, device=None):
if check_device(device):
if check_cuda(device):
device = return_xpu(device)
if isinstance(data, np.ndarray) and data.dtype == float and not (
(isinstance(device, torch.device) and device.type == "cpu") or (isinstance(device, str) and "cpu" in device)):
if isinstance(data, np.ndarray) and data.dtype == float and not check_device_type(device, "cpu"):
return original_as_tensor(data, dtype=torch.float32, device=device)
else:
return original_as_tensor(data, dtype=dtype, device=device)
@@ -198,10 +203,10 @@ original_torch_tensor = torch.tensor
@wraps(torch.tensor)
def torch_tensor(data, *args, dtype=None, device=None, **kwargs):
global device_supports_fp64
if check_device(device):
if check_cuda(device):
device = return_xpu(device)
if not device_supports_fp64:
if (isinstance(device, torch.device) and device.type == "xpu") or (isinstance(device, str) and "xpu" in device):
if check_device_type(device, "xpu"):
if dtype == torch.float64:
dtype = torch.float32
elif dtype is None and (hasattr(data, "dtype") and (data.dtype == torch.float64 or data.dtype == float)):
@@ -211,7 +216,7 @@ def torch_tensor(data, *args, dtype=None, device=None, **kwargs):
original_Tensor_to = torch.Tensor.to
@wraps(torch.Tensor.to)
def Tensor_to(self, device=None, *args, **kwargs):
if check_device(device):
if check_cuda(device):
return original_Tensor_to(self, return_xpu(device), *args, **kwargs)
else:
return original_Tensor_to(self, device, *args, **kwargs)
@@ -219,17 +224,15 @@ def Tensor_to(self, device=None, *args, **kwargs):
original_Tensor_cuda = torch.Tensor.cuda
@wraps(torch.Tensor.cuda)
def Tensor_cuda(self, device=None, *args, **kwargs):
if check_device(device):
return original_Tensor_cuda(self, return_xpu(device), *args, **kwargs)
if device is None or check_cuda(device):
return self.to(return_xpu(device), *args, **kwargs)
else:
return original_Tensor_cuda(self, device, *args, **kwargs)
original_Tensor_pin_memory = torch.Tensor.pin_memory
@wraps(torch.Tensor.pin_memory)
def Tensor_pin_memory(self, device=None, *args, **kwargs):
if device is None:
device = "xpu"
if check_device(device):
if device is None or check_cuda(device):
return original_Tensor_pin_memory(self, return_xpu(device), *args, **kwargs)
else:
return original_Tensor_pin_memory(self, device, *args, **kwargs)
@@ -237,23 +240,32 @@ def Tensor_pin_memory(self, device=None, *args, **kwargs):
original_UntypedStorage_init = torch.UntypedStorage.__init__
@wraps(torch.UntypedStorage.__init__)
def UntypedStorage_init(*args, device=None, **kwargs):
if check_device(device):
if check_cuda(device):
return original_UntypedStorage_init(*args, device=return_xpu(device), **kwargs)
else:
return original_UntypedStorage_init(*args, device=device, **kwargs)
original_UntypedStorage_cuda = torch.UntypedStorage.cuda
@wraps(torch.UntypedStorage.cuda)
def UntypedStorage_cuda(self, device=None, *args, **kwargs):
if check_device(device):
return original_UntypedStorage_cuda(self, return_xpu(device), *args, **kwargs)
else:
return original_UntypedStorage_cuda(self, device, *args, **kwargs)
if float(torch.__version__[:3]) >= 2.4:
original_UntypedStorage_to = torch.UntypedStorage.to
@wraps(torch.UntypedStorage.to)
def UntypedStorage_to(self, *args, device=None, **kwargs):
if check_cuda(device):
return original_UntypedStorage_to(self, *args, device=return_xpu(device), **kwargs)
else:
return original_UntypedStorage_to(self, *args, device=device, **kwargs)
original_UntypedStorage_cuda = torch.UntypedStorage.cuda
@wraps(torch.UntypedStorage.cuda)
def UntypedStorage_cuda(self, device=None, non_blocking=False, **kwargs):
if device is None or check_cuda(device):
return self.to(device=return_xpu(device), non_blocking=non_blocking, **kwargs)
else:
return original_UntypedStorage_cuda(self, device=device, non_blocking=non_blocking, **kwargs)
original_torch_empty = torch.empty
@wraps(torch.empty)
def torch_empty(*args, device=None, **kwargs):
if check_device(device):
if check_cuda(device):
return original_torch_empty(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_empty(*args, device=device, **kwargs)
@@ -263,7 +275,7 @@ original_torch_randn = torch.randn
def torch_randn(*args, device=None, dtype=None, **kwargs):
if dtype is bytes:
dtype = None
if check_device(device):
if check_cuda(device):
return original_torch_randn(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_randn(*args, device=device, **kwargs)
@@ -271,7 +283,7 @@ def torch_randn(*args, device=None, dtype=None, **kwargs):
original_torch_ones = torch.ones
@wraps(torch.ones)
def torch_ones(*args, device=None, **kwargs):
if check_device(device):
if check_cuda(device):
return original_torch_ones(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_ones(*args, device=device, **kwargs)
@@ -279,7 +291,7 @@ def torch_ones(*args, device=None, **kwargs):
original_torch_zeros = torch.zeros
@wraps(torch.zeros)
def torch_zeros(*args, device=None, **kwargs):
if check_device(device):
if check_cuda(device):
return original_torch_zeros(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_zeros(*args, device=device, **kwargs)
@@ -287,7 +299,7 @@ def torch_zeros(*args, device=None, **kwargs):
original_torch_full = torch.full
@wraps(torch.full)
def torch_full(*args, device=None, **kwargs):
if check_device(device):
if check_cuda(device):
return original_torch_full(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_full(*args, device=device, **kwargs)
@@ -295,17 +307,23 @@ def torch_full(*args, device=None, **kwargs):
original_torch_linspace = torch.linspace
@wraps(torch.linspace)
def torch_linspace(*args, device=None, **kwargs):
if check_device(device):
if check_cuda(device):
return original_torch_linspace(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_linspace(*args, device=device, **kwargs)
original_torch_eye = torch.eye
@wraps(torch.eye)
def torch_eye(*args, device=None, **kwargs):
if check_cuda(device):
return original_torch_eye(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_eye(*args, device=device, **kwargs)
original_torch_load = torch.load
@wraps(torch.load)
def torch_load(f, map_location=None, *args, **kwargs):
if map_location is None:
map_location = "xpu"
if check_device(map_location):
if map_location is None or check_cuda(map_location):
return original_torch_load(f, *args, map_location=return_xpu(map_location), **kwargs)
else:
return original_torch_load(f, *args, map_location=map_location, **kwargs)
@@ -313,14 +331,14 @@ def torch_load(f, map_location=None, *args, **kwargs):
original_torch_Generator = torch.Generator
@wraps(torch.Generator)
def torch_Generator(device=None):
if check_device(device):
if check_cuda(device):
return original_torch_Generator(return_xpu(device))
else:
return original_torch_Generator(device)
@wraps(torch.cuda.synchronize)
def torch_cuda_synchronize(device=None):
if check_device(device):
if check_cuda(device):
return torch.xpu.synchronize(return_xpu(device))
else:
return torch.xpu.synchronize(device)
@@ -329,20 +347,23 @@ def torch_cuda_synchronize(device=None):
# Hijack Functions:
def ipex_hijacks(legacy=True):
global device_supports_fp64, can_allocate_plus_4gb
if legacy and float(torch.__version__[:3]) < 2.5:
if float(torch.__version__[:3]) >= 2.4:
torch.UntypedStorage.cuda = UntypedStorage_cuda
torch.UntypedStorage.to = UntypedStorage_to
else: # ipex 2.3 and below
torch.nn.functional.interpolate = interpolate
torch.tensor = torch_tensor
torch.Tensor.to = Tensor_to
torch.Tensor.cuda = Tensor_cuda
torch.Tensor.pin_memory = Tensor_pin_memory
torch.UntypedStorage.__init__ = UntypedStorage_init
torch.UntypedStorage.cuda = UntypedStorage_cuda
torch.empty = torch_empty
torch.randn = torch_randn
torch.ones = torch_ones
torch.zeros = torch_zeros
torch.full = torch_full
torch.linspace = torch_linspace
torch.eye = torch_eye
torch.load = torch_load
torch.Generator = torch_Generator
torch.cuda.synchronize = torch_cuda_synchronize