366 lines
12 KiB
Python
366 lines
12 KiB
Python
import contextlib
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import importlib
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import torch
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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# pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
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class CondFunc: # pylint: disable=missing-class-docstring
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def __new__(cls, orig_func, sub_func, cond_func):
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self = super(CondFunc, cls).__new__(cls)
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if isinstance(orig_func, str):
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func_path = orig_func.split(".")
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for i in range(len(func_path) - 1, -1, -1):
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try:
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resolved_obj = importlib.import_module(".".join(func_path[:i]))
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break
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except ImportError:
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pass
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for attr_name in func_path[i:-1]:
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resolved_obj = getattr(resolved_obj, attr_name)
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orig_func = getattr(resolved_obj, func_path[-1])
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setattr(
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resolved_obj,
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func_path[-1],
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lambda *args, **kwargs: self(*args, **kwargs),
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)
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self.__init__(orig_func, sub_func, cond_func)
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return lambda *args, **kwargs: self(*args, **kwargs)
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def __init__(self, orig_func, sub_func, cond_func):
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self.__orig_func = orig_func
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self.__sub_func = sub_func
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self.__cond_func = cond_func
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def __call__(self, *args, **kwargs):
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if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
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return self.__sub_func(self.__orig_func, *args, **kwargs)
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else:
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return self.__orig_func(*args, **kwargs)
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_utils = torch.utils.data._utils
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def _shutdown_workers(self):
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if (
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torch.utils.data._utils is None
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or torch.utils.data._utils.python_exit_status is True
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or torch.utils.data._utils.python_exit_status is None
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):
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return
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if hasattr(self, "_shutdown") and not self._shutdown:
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self._shutdown = True
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try:
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if hasattr(self, "_pin_memory_thread"):
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self._pin_memory_thread_done_event.set()
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self._worker_result_queue.put((None, None))
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self._pin_memory_thread.join()
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self._worker_result_queue.cancel_join_thread()
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self._worker_result_queue.close()
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self._workers_done_event.set()
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for worker_id in range(len(self._workers)):
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if self._persistent_workers or self._workers_status[worker_id]:
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self._mark_worker_as_unavailable(worker_id, shutdown=True)
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for w in self._workers: # pylint: disable=invalid-name
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w.join(timeout=torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL)
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for q in self._index_queues: # pylint: disable=invalid-name
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q.cancel_join_thread()
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q.close()
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finally:
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if self._worker_pids_set:
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torch.utils.data._utils.signal_handling._remove_worker_pids(id(self))
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self._worker_pids_set = False
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for w in self._workers: # pylint: disable=invalid-name
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if w.is_alive():
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w.terminate()
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class DummyDataParallel(
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torch.nn.Module
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): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
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def __new__(
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cls, module, device_ids=None, output_device=None, dim=0
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): # pylint: disable=unused-argument
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if isinstance(device_ids, list) and len(device_ids) > 1:
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print("IPEX backend doesn't support DataParallel on multiple XPU devices")
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return module.to("xpu")
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def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
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return contextlib.nullcontext()
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def check_device(device):
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return bool(
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(isinstance(device, torch.device) and device.type == "cuda")
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or (isinstance(device, str) and "cuda" in device)
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or isinstance(device, int)
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)
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def return_xpu(device):
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return (
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f"xpu:{device[-1]}"
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if isinstance(device, str) and ":" in device
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else (
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f"xpu:{device}"
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if isinstance(device, int)
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else torch.device("xpu") if isinstance(device, torch.device) else "xpu"
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)
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)
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def ipex_no_cuda(orig_func, *args, **kwargs):
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torch.cuda.is_available = lambda: False
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orig_func(*args, **kwargs)
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torch.cuda.is_available = torch.xpu.is_available
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original_autocast = torch.autocast
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def ipex_autocast(*args, **kwargs):
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if len(args) > 0 and args[0] == "cuda":
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return original_autocast("xpu", *args[1:], **kwargs)
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else:
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return original_autocast(*args, **kwargs)
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original_torch_cat = torch.cat
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def torch_cat(tensor, *args, **kwargs):
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if len(tensor) == 3 and (
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tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype
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):
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return original_torch_cat(
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[tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)],
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*args,
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**kwargs,
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)
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else:
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return original_torch_cat(tensor, *args, **kwargs)
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original_interpolate = torch.nn.functional.interpolate
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def interpolate(
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tensor,
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size=None,
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scale_factor=None,
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mode="nearest",
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align_corners=None,
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recompute_scale_factor=None,
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antialias=False,
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): # pylint: disable=too-many-arguments
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if antialias or align_corners is not None:
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return_device = tensor.device
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return_dtype = tensor.dtype
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return original_interpolate(
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tensor.to("cpu", dtype=torch.float32),
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size=size,
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scale_factor=scale_factor,
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mode=mode,
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align_corners=align_corners,
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recompute_scale_factor=recompute_scale_factor,
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antialias=antialias,
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).to(return_device, dtype=return_dtype)
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else:
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return original_interpolate(
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tensor,
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size=size,
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scale_factor=scale_factor,
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mode=mode,
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align_corners=align_corners,
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recompute_scale_factor=recompute_scale_factor,
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antialias=antialias,
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)
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original_linalg_solve = torch.linalg.solve
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def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name
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if A.device != torch.device("cpu") or B.device != torch.device("cpu"):
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return_device = A.device
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return original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(
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return_device
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)
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else:
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return original_linalg_solve(A, B, *args, **kwargs)
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def ipex_hijacks():
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CondFunc(
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"torch.Tensor.to",
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lambda orig_func, self, device=None, *args, **kwargs: orig_func(
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self, return_xpu(device), *args, **kwargs
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),
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lambda orig_func, self, device=None, *args, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.Tensor.cuda",
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lambda orig_func, self, device=None, *args, **kwargs: orig_func(
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self, return_xpu(device), *args, **kwargs
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),
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lambda orig_func, self, device=None, *args, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.empty",
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lambda orig_func, *args, device=None, **kwargs: orig_func(
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*args, device=return_xpu(device), **kwargs
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),
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lambda orig_func, *args, device=None, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.load",
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lambda orig_func, *args, map_location=None, **kwargs: orig_func(
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*args, return_xpu(map_location), **kwargs
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),
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lambda orig_func, *args, map_location=None, **kwargs: map_location is None
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or check_device(map_location),
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)
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CondFunc(
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"torch.randn",
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lambda orig_func, *args, device=None, **kwargs: orig_func(
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*args, device=return_xpu(device), **kwargs
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),
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lambda orig_func, *args, device=None, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.ones",
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lambda orig_func, *args, device=None, **kwargs: orig_func(
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*args, device=return_xpu(device), **kwargs
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),
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lambda orig_func, *args, device=None, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.zeros",
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lambda orig_func, *args, device=None, **kwargs: orig_func(
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*args, device=return_xpu(device), **kwargs
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),
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lambda orig_func, *args, device=None, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.tensor",
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lambda orig_func, *args, device=None, **kwargs: orig_func(
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*args, device=return_xpu(device), **kwargs
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),
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lambda orig_func, *args, device=None, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.linspace",
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lambda orig_func, *args, device=None, **kwargs: orig_func(
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*args, device=return_xpu(device), **kwargs
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),
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lambda orig_func, *args, device=None, **kwargs: check_device(device),
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)
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CondFunc(
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"torch.Generator",
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lambda orig_func, device=None: torch.xpu.Generator(device),
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lambda orig_func, device=None: device is not None
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and device != torch.device("cpu")
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and device != "cpu",
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)
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CondFunc(
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"torch.batch_norm",
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lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(
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input,
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(
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weight
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if weight is not None
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else torch.ones(input.size()[1], device=input.device)
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),
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(
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bias
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if bias is not None
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else torch.zeros(input.size()[1], device=input.device)
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),
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*args,
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**kwargs,
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),
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lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"),
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)
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CondFunc(
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"torch.instance_norm",
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lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(
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input,
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(
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weight
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if weight is not None
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else torch.ones(input.size()[1], device=input.device)
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),
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(
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bias
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if bias is not None
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else torch.zeros(input.size()[1], device=input.device)
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),
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*args,
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**kwargs,
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),
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lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"),
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)
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# Functions with dtype errors:
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CondFunc(
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"torch.nn.modules.GroupNorm.forward",
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lambda orig_func, self, input: orig_func(
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self, input.to(self.weight.data.dtype)
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),
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lambda orig_func, self, input: input.dtype != self.weight.data.dtype,
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)
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CondFunc(
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"torch.nn.modules.linear.Linear.forward",
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lambda orig_func, self, input: orig_func(
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self, input.to(self.weight.data.dtype)
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),
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lambda orig_func, self, input: input.dtype != self.weight.data.dtype,
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)
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CondFunc(
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"torch.nn.modules.conv.Conv2d.forward",
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lambda orig_func, self, input: orig_func(
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self, input.to(self.weight.data.dtype)
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),
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lambda orig_func, self, input: input.dtype != self.weight.data.dtype,
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)
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CondFunc(
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"torch.nn.functional.layer_norm",
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lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs: orig_func(
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input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs
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),
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lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs: weight
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is not None
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and input.dtype != weight.data.dtype,
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)
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# Diffusers Float64 (ARC GPUs doesn't support double or Float64):
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if not torch.xpu.has_fp64_dtype():
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CondFunc(
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"torch.from_numpy",
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lambda orig_func, ndarray: orig_func(ndarray.astype("float32")),
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lambda orig_func, ndarray: ndarray.dtype == float,
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)
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# Broken functions when torch.cuda.is_available is True:
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CondFunc(
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"torch.utils.data.dataloader._BaseDataLoaderIter.__init__",
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lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
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lambda orig_func, *args, **kwargs: True,
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)
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# Functions that make compile mad with CondFunc:
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torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = (
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_shutdown_workers
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)
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torch.nn.DataParallel = DummyDataParallel
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torch.autocast = ipex_autocast
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torch.cat = torch_cat
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torch.linalg.solve = linalg_solve
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torch.nn.functional.interpolate = interpolate
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torch.backends.cuda.sdp_kernel = return_null_context
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