Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-18 16:54:33 +02:00
Code Issues Packages Projects Releases Wiki Activity

ipex fix cuda error when using pin memory

Browse Source
This commit is contained in:
Disty0
2023-07-17 17:14:13 +03:00
parent da11f32db7
commit 88794e3724
1 changed files with 14 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/devices.py
+14
View File
@@ -181,12 +181,20 @@ else:
if backend == 'ipex':
import os
def ipex_no_cuda(orig_func, *args, **kwargs):
torch.cuda.is_available = lambda: False
orig_func(*args, **kwargs)
torch.cuda.is_available = torch.xpu.is_available
#Fix functions with ipex
torch.cuda.is_available = torch.xpu.is_available
torch.cuda.device = torch.xpu.device
torch.cuda.device_count = torch.xpu.device_count
torch.cuda.current_device = torch.xpu.current_device
torch.cuda.get_device_name = torch.xpu.get_device_name
torch.cuda.get_device_properties = torch.xpu.get_device_properties
torch._utils._get_available_device_type = lambda: "xpu"
torch.cuda.set_device = torch.xpu.set_device
torch.cuda.empty_cache = torch.xpu.empty_cache if "WSL2" not in os.popen("uname -a").read() else lambda: None
torch.cuda.ipc_collect = lambda: None
@@ -205,6 +213,11 @@ if backend == 'ipex':
pass
from modules.sd_hijack_utils import CondFunc
#Broken functions when torch.cuda.is_available is True:
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 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)),
@@ -226,6 +239,7 @@ if backend == 'ipex':
CondFunc('torch.Generator',
lambda orig_func, device: torch.xpu.Generator(device),
lambda orig_func, device: device != torch.device("cpu") and device != "cpu")
#Latent antialias:
CondFunc('torch.nn.functional.interpolate',
lambda orig_func, input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False: orig_func(input.to("cpu"), size=size, scale_factor=scale_factor, mode=mode, align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(get_cuda_device_string()),
lambda orig_func, input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False: antialias)