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-rw-r--r--modules/cmd_args.py1
-rw-r--r--modules/devices.py13
-rw-r--r--modules/launch_utils.py22
-rw-r--r--modules/sd_samplers_timesteps_impl.py4
-rw-r--r--modules/xpu_specific.py50
5 files changed, 88 insertions, 2 deletions
diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index a9fb9bfa..da93eb26 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -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)
diff --git a/modules/devices.py b/modules/devices.py
index f97f9cfb..ea1f712f 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -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():
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 264ec9ca..586cdc7e 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -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; '
diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py
index a72daafd..930a64af 100644
--- a/modules/sd_samplers_timesteps_impl.py
+++ b/modules/sd_samplers_timesteps_impl.py
@@ -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
diff --git a/modules/xpu_specific.py b/modules/xpu_specific.py
new file mode 100644
index 00000000..d933c790
--- /dev/null
+++ b/modules/xpu_specific.py
@@ -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)
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 17:20:09 +0000