Merge pull request #14171 from Nuullll/ipex

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)

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":

    if has_mps():

        return "mps"

    if has_xpu():

        return xpu_specific.get_xpu_device_string()

    return "cpu"

    if has_mps():

        mac_specific.torch_mps_gc()

    if has_xpu():

        xpu_specific.torch_xpu_gc()

def enable_tf32():

    if torch.cuda.is_available():

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')

        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; '

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()))

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

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)