Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-17 08:19:11 +02:00
Code Issues Packages Projects Releases Wiki Activity

Seperate OpenVINO from IPEX

Browse Source
This commit is contained in:
Disty0
2023-08-19 17:52:15 +03:00
parent 05c3bd075a
commit f9718f068c
8 changed files with 15 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files
installer.py
+4 -2
View File
@@ -399,10 +399,10 @@ def check_torch():
os.environ.setdefault('NEOReadDebugKeys', '1')
os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
if "linux" in sys.platform:
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.1a0 torchvision==0.15.2a0 intel_extension_for_pytorch==2.0.110+xpu openvino==2023.1.0.dev20230811 -f https://developer.intel.com/ipex-whl-stable-xpu')
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.1a0 torchvision==0.15.2a0 intel_extension_for_pytorch==2.0.110+xpu -f https://developer.intel.com/ipex-whl-stable-xpu')
os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow==2.13.0 intel-extension-for-tensorflow[gpu]')
else:
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.0a0 intel_extension_for_pytorch==2.0.110+gitba7f6c1 openvino==2023.1.0.dev20230811 -f https://developer.intel.com/ipex-whl-stable-xpu')
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.0a0 intel_extension_for_pytorch==2.0.110+gitba7f6c1 -f https://developer.intel.com/ipex-whl-stable-xpu')
else:
machine = platform.machine()
if sys.platform == 'darwin':
@@ -467,6 +467,8 @@ def check_torch():
log.debug(f'Cannot install xformers package: {e}')
if opts.get('cuda_compile_backend', '') == 'hidet':
install('hidet', 'hidet')
if opts.get('cuda_compile_backend', '') == 'openvino_fx':
install('openvino==2023.1.0.dev20230811', 'openvino')
if args.profile:
print_profile(pr, 'Torch')
modules/devices.py
+1 -1
View File
@@ -167,7 +167,7 @@ def set_cuda_params():
args = cmd_args.parser.parse_args()
if args.use_ipex or (hasattr(torch, 'xpu') and torch.xpu.is_available()):
backend = 'ipex'
from modules.ipex_specific import ipex_init
from modules.intel.ipex import ipex_init
ipex_init()
elif args.use_directml:
backend = 'directml'
modules/ipex_specific/__init__.py → modules/intel/ipex/__init__.py
-4
View File
@@ -163,7 +163,3 @@ def ipex_init():
ipex_hijacks()
ipex_diffusers()
try:
from .openvino import openvino_fx
except Exception:
pass
modules/ipex_specific/diffusers.py → modules/intel/ipex/diffusers.py
View File
modules/ipex_specific/hijacks.py → modules/intel/ipex/hijacks.py
+1 -1
View File
@@ -11,7 +11,7 @@ def ipex_no_cuda(orig_func, *args, **kwargs): # pylint: disable=redefined-outer-
#Autocast
original_autocast = torch.autocast
def ipex_autocast(*args, **kwargs):
if args[0] == "cuda":
if args[0] == "cuda" or args[0] == "xpu":
if "dtype" in kwargs:
return original_autocast("xpu", *args[1:], **kwargs)
else:
modules/ipex_specific/openvino.py → modules/intel/openvino/__init__.py
-1
View File
@@ -1,6 +1,5 @@
import os
import torch
import intel_extension_for_pytorch as ipex
from openvino.frontend.pytorch.torchdynamo.execute import execute
from openvino.frontend.pytorch.torchdynamo.partition import Partitioner
from torch._dynamo.backends.common import fake_tensor_unsupported
modules/sd_hijack.py
+2
View File
@@ -188,6 +188,8 @@ class StableDiffusionModelHijack:
import logging
shared.log.info(f"Compiling pipeline={m.model.__class__.__name__} mode={opts.cuda_compile_backend}")
import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
if shared.opts.cuda_compile_backend == "openvino_fx":
from modules.intel.openvino import openvino_fx
log_level = logging.WARNING if opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
if hasattr(torch, '_logging'):
torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
modules/sd_models.py
+7 -2
View File
@@ -749,7 +749,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
sd_model.unet.to(memory_format=torch.channels_last)
base_sent_to_cpu=False
if (shared.opts.cuda_compile or shared.opts.ipex_optimize) and torch.cuda.is_available():
if (shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none') or shared.opts.ipex_optimize:
if op == 'refiner' and not sd_model.has_accelerate:
gpu_vram = memory_stats().get('gpu', {})
free_vram = gpu_vram.get('total', 0) - gpu_vram.get('used', 0)
@@ -774,20 +774,25 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
try:
if shared.opts.ipex_optimize:
sd_model.unet.training = False
sd_model.vae.training = False
sd_model.unet = torch.xpu.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
sd_model.vae = torch.xpu.optimize(sd_model.vae, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
shared.log.info("Applied IPEX Optimize.")
except Exception as err:
shared.log.warning(f"IPEX Optimize not supported: {err}")
try:
if shared.opts.cuda_compile:
if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
shared.log.info(f"Compiling pipeline={sd_model.__class__.__name__} shape={8 * sd_model.unet.config.sample_size} mode={shared.opts.cuda_compile_backend}")
import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
if shared.opts.cuda_compile_backend == "openvino_fx":
from modules.intel.openvino import openvino_fx
log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
if hasattr(torch, '_logging'):
torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
torch._dynamo.config.verbose = shared.opts.cuda_compile_verbose # pylint: disable=protected-access
torch._dynamo.config.suppress_errors = shared.opts.cuda_compile_errors # pylint: disable=protected-access
sd_model.unet = torch.compile(sd_model.unet, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
sd_model.vae.decode = torch.compile(sd_model.vae.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
sd_model("dummy prompt")
shared.log.info("Complilation done.")
except Exception as err: