mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
Merge pull request #14171 from Nuullll/ipex
Initial IPEX support for Intel Arc GPU
This commit is contained in:
commit
af5f0734c9
@ -70,6 +70,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
|
||||
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
|
||||
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
|
||||
parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device")
|
||||
parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
|
||||
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
|
||||
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
|
||||
|
@ -8,6 +8,13 @@ from modules import errors, shared
|
||||
if sys.platform == "darwin":
|
||||
from modules import mac_specific
|
||||
|
||||
if shared.cmd_opts.use_ipex:
|
||||
from modules import xpu_specific
|
||||
|
||||
|
||||
def has_xpu() -> bool:
|
||||
return shared.cmd_opts.use_ipex and xpu_specific.has_xpu
|
||||
|
||||
|
||||
def has_mps() -> bool:
|
||||
if sys.platform != "darwin":
|
||||
@ -30,6 +37,9 @@ def get_optimal_device_name():
|
||||
if has_mps():
|
||||
return "mps"
|
||||
|
||||
if has_xpu():
|
||||
return xpu_specific.get_xpu_device_string()
|
||||
|
||||
return "cpu"
|
||||
|
||||
|
||||
@ -54,6 +64,9 @@ def torch_gc():
|
||||
if has_mps():
|
||||
mac_specific.torch_mps_gc()
|
||||
|
||||
if has_xpu():
|
||||
xpu_specific.torch_xpu_gc()
|
||||
|
||||
|
||||
def enable_tf32():
|
||||
if torch.cuda.is_available():
|
||||
|
@ -310,6 +310,26 @@ def requirements_met(requirements_file):
|
||||
def prepare_environment():
|
||||
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
|
||||
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
|
||||
if args.use_ipex:
|
||||
if platform.system() == "Windows":
|
||||
# The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
|
||||
# This is NOT an Intel official release so please use it at your own risk!!
|
||||
# See https://github.com/Nuullll/intel-extension-for-pytorch/releases/tag/v2.0.110%2Bxpu-master%2Bdll-bundle for details.
|
||||
#
|
||||
# Strengths (over official IPEX 2.0.110 windows release):
|
||||
# - AOT build (for Arc GPU only) to eliminate JIT compilation overhead: https://github.com/intel/intel-extension-for-pytorch/issues/399
|
||||
# - Bundles minimal oneAPI 2023.2 dependencies into the python wheels, so users don't need to install oneAPI for the whole system.
|
||||
# - Provides a compatible torchvision wheel: https://github.com/intel/intel-extension-for-pytorch/issues/465
|
||||
# Limitation:
|
||||
# - Only works for python 3.10
|
||||
url_prefix = "https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.0.110%2Bxpu-master%2Bdll-bundle"
|
||||
torch_command = os.environ.get('TORCH_COMMAND', f"pip install {url_prefix}/torch-2.0.0a0+gite9ebda2-cp310-cp310-win_amd64.whl {url_prefix}/torchvision-0.15.2a0+fa99a53-cp310-cp310-win_amd64.whl {url_prefix}/intel_extension_for_pytorch-2.0.110+gitc6ea20b-cp310-cp310-win_amd64.whl")
|
||||
else:
|
||||
# Using official IPEX release for linux since it's already an AOT build.
|
||||
# However, users still have to install oneAPI toolkit and activate oneAPI environment manually.
|
||||
# See https://intel.github.io/intel-extension-for-pytorch/index.html#installation for details.
|
||||
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
|
||||
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
|
||||
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
|
||||
|
||||
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
|
||||
@ -352,6 +372,8 @@ def prepare_environment():
|
||||
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
|
||||
startup_timer.record("install torch")
|
||||
|
||||
if args.use_ipex:
|
||||
args.skip_torch_cuda_test = True
|
||||
if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
|
||||
raise RuntimeError(
|
||||
'Torch is not able to use GPU; '
|
||||
|
@ -11,7 +11,7 @@ from modules.models.diffusion.uni_pc import uni_pc
|
||||
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
|
||||
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
||||
alphas = alphas_cumprod[timesteps]
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
|
||||
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
||||
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
|
||||
|
||||
@ -43,7 +43,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
|
||||
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
|
||||
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
||||
alphas = alphas_cumprod[timesteps]
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
|
||||
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
||||
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
|
50
modules/xpu_specific.py
Normal file
50
modules/xpu_specific.py
Normal file
@ -0,0 +1,50 @@
|
||||
from modules import shared
|
||||
from modules.sd_hijack_utils import CondFunc
|
||||
|
||||
has_ipex = False
|
||||
try:
|
||||
import torch
|
||||
import intel_extension_for_pytorch as ipex # noqa: F401
|
||||
has_ipex = True
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def check_for_xpu():
|
||||
return has_ipex and hasattr(torch, 'xpu') and torch.xpu.is_available()
|
||||
|
||||
|
||||
def get_xpu_device_string():
|
||||
if shared.cmd_opts.device_id is not None:
|
||||
return f"xpu:{shared.cmd_opts.device_id}"
|
||||
return "xpu"
|
||||
|
||||
|
||||
def torch_xpu_gc():
|
||||
with torch.xpu.device(get_xpu_device_string()):
|
||||
torch.xpu.empty_cache()
|
||||
|
||||
|
||||
has_xpu = check_for_xpu()
|
||||
|
||||
if has_xpu:
|
||||
# W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device
|
||||
CondFunc('torch.Generator',
|
||||
lambda orig_func, device=None: torch.xpu.Generator(device),
|
||||
lambda orig_func, device=None: device is not None and device.type == "xpu")
|
||||
|
||||
# W/A for some OPs that could not handle different input dtypes
|
||||
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)
|
||||
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)
|
||||
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)
|
Loading…
Reference in New Issue
Block a user