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| import contextlib | |
| import importlib | |
| import torch | |
| import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import | |
| # pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return | |
| class CondFunc: # pylint: disable=missing-class-docstring | |
| def __new__(cls, orig_func, sub_func, cond_func): | |
| self = super(CondFunc, cls).__new__(cls) | |
| if isinstance(orig_func, str): | |
| func_path = orig_func.split('.') | |
| for i in range(len(func_path)-1, -1, -1): | |
| try: | |
| resolved_obj = importlib.import_module('.'.join(func_path[:i])) | |
| break | |
| except ImportError: | |
| pass | |
| for attr_name in func_path[i:-1]: | |
| resolved_obj = getattr(resolved_obj, attr_name) | |
| orig_func = getattr(resolved_obj, func_path[-1]) | |
| setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs)) | |
| self.__init__(orig_func, sub_func, cond_func) | |
| return lambda *args, **kwargs: self(*args, **kwargs) | |
| def __init__(self, orig_func, sub_func, cond_func): | |
| self.__orig_func = orig_func | |
| self.__sub_func = sub_func | |
| self.__cond_func = cond_func | |
| def __call__(self, *args, **kwargs): | |
| if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs): | |
| return self.__sub_func(self.__orig_func, *args, **kwargs) | |
| else: | |
| return self.__orig_func(*args, **kwargs) | |
| _utils = torch.utils.data._utils | |
| def _shutdown_workers(self): | |
| if torch.utils.data._utils is None or torch.utils.data._utils.python_exit_status is True or torch.utils.data._utils.python_exit_status is None: | |
| return | |
| if hasattr(self, "_shutdown") and not self._shutdown: | |
| self._shutdown = True | |
| try: | |
| if hasattr(self, '_pin_memory_thread'): | |
| self._pin_memory_thread_done_event.set() | |
| self._worker_result_queue.put((None, None)) | |
| self._pin_memory_thread.join() | |
| self._worker_result_queue.cancel_join_thread() | |
| self._worker_result_queue.close() | |
| self._workers_done_event.set() | |
| for worker_id in range(len(self._workers)): | |
| if self._persistent_workers or self._workers_status[worker_id]: | |
| self._mark_worker_as_unavailable(worker_id, shutdown=True) | |
| for w in self._workers: # pylint: disable=invalid-name | |
| w.join(timeout=torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL) | |
| for q in self._index_queues: # pylint: disable=invalid-name | |
| q.cancel_join_thread() | |
| q.close() | |
| finally: | |
| if self._worker_pids_set: | |
| torch.utils.data._utils.signal_handling._remove_worker_pids(id(self)) | |
| self._worker_pids_set = False | |
| for w in self._workers: # pylint: disable=invalid-name | |
| if w.is_alive(): | |
| w.terminate() | |
| class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods | |
| def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument | |
| if isinstance(device_ids, list) and len(device_ids) > 1: | |
| print("IPEX backend doesn't support DataParallel on multiple XPU devices") | |
| return module.to("xpu") | |
| def return_null_context(*args, **kwargs): # pylint: disable=unused-argument | |
| return contextlib.nullcontext() | |
| def check_device(device): | |
| return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int)) | |
| def return_xpu(device): | |
| return f"xpu:{device[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device("xpu") if isinstance(device, torch.device) else "xpu" | |
| 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 | |
| original_autocast = torch.autocast | |
| def ipex_autocast(*args, **kwargs): | |
| if len(args) > 0 and args[0] == "cuda": | |
| return original_autocast("xpu", *args[1:], **kwargs) | |
| else: | |
| return original_autocast(*args, **kwargs) | |
| original_torch_cat = torch.cat | |
| def torch_cat(tensor, *args, **kwargs): | |
| if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype): | |
| return original_torch_cat([tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)], *args, **kwargs) | |
| else: | |
| return original_torch_cat(tensor, *args, **kwargs) | |
| original_interpolate = torch.nn.functional.interpolate | |
| def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments | |
| if antialias or align_corners is not None: | |
| return_device = tensor.device | |
| return_dtype = tensor.dtype | |
| return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode, | |
| align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(return_device, dtype=return_dtype) | |
| else: | |
| return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode, | |
| align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias) | |
| original_linalg_solve = torch.linalg.solve | |
| def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name | |
| if A.device != torch.device("cpu") or B.device != torch.device("cpu"): | |
| return_device = A.device | |
| return original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(return_device) | |
| else: | |
| return original_linalg_solve(A, B, *args, **kwargs) | |
| def ipex_hijacks(): | |
| CondFunc('torch.Tensor.to', | |
| lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs), | |
| lambda orig_func, self, device=None, *args, **kwargs: check_device(device)) | |
| CondFunc('torch.Tensor.cuda', | |
| lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs), | |
| lambda orig_func, self, device=None, *args, **kwargs: check_device(device)) | |
| CondFunc('torch.empty', | |
| lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), | |
| lambda orig_func, *args, device=None, **kwargs: check_device(device)) | |
| CondFunc('torch.load', | |
| lambda orig_func, *args, map_location=None, **kwargs: orig_func(*args, return_xpu(map_location), **kwargs), | |
| lambda orig_func, *args, map_location=None, **kwargs: map_location is None or check_device(map_location)) | |
| CondFunc('torch.randn', | |
| lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), | |
| lambda orig_func, *args, device=None, **kwargs: check_device(device)) | |
| CondFunc('torch.ones', | |
| lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), | |
| lambda orig_func, *args, device=None, **kwargs: check_device(device)) | |
| CondFunc('torch.zeros', | |
| lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), | |
| lambda orig_func, *args, device=None, **kwargs: check_device(device)) | |
| CondFunc('torch.tensor', | |
| lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), | |
| lambda orig_func, *args, device=None, **kwargs: check_device(device)) | |
| CondFunc('torch.linspace', | |
| lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), | |
| lambda orig_func, *args, device=None, **kwargs: check_device(device)) | |
| CondFunc('torch.Generator', | |
| lambda orig_func, device=None: torch.xpu.Generator(device), | |
| lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu") | |
| CondFunc('torch.batch_norm', | |
| lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input, | |
| weight if weight is not None else torch.ones(input.size()[1], device=input.device), | |
| bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs), | |
| lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu")) | |
| CondFunc('torch.instance_norm', | |
| lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input, | |
| weight if weight is not None else torch.ones(input.size()[1], device=input.device), | |
| bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs), | |
| lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu")) | |
| #Functions with dtype errors: | |
| 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) | |
| 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) | |
| #Diffusers Float64 (ARC GPUs doesn't support double or Float64): | |
| if not torch.xpu.has_fp64_dtype(): | |
| CondFunc('torch.from_numpy', | |
| lambda orig_func, ndarray: orig_func(ndarray.astype('float32')), | |
| lambda orig_func, ndarray: ndarray.dtype == float) | |
| #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 that make compile mad with CondFunc: | |
| torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers | |
| torch.nn.DataParallel = DummyDataParallel | |
| torch.autocast = ipex_autocast | |
| torch.cat = torch_cat | |
| torch.linalg.solve = linalg_solve | |
| torch.nn.functional.interpolate = interpolate | |
| torch.backends.cuda.sdp_kernel = return_null_context |