Source code for torch.utils

import os.path as _osp
import torch

from .throughput_benchmark import ThroughputBenchmark
from .cpp_backtrace import get_cpp_backtrace
from .backend_registration import rename_privateuse1_backend, generate_methods_for_privateuse1_backend
from . import deterministic
from . import collect_env
import weakref
import copyreg

[docs]def set_module(obj, mod): """ Set the module attribute on a python object for a given object for nicer printing """ if not isinstance(mod, str): raise TypeError("The mod argument should be a string") obj.__module__ = mod
if torch._running_with_deploy(): # not valid inside torch_deploy interpreter, no paths exists for frozen modules cmake_prefix_path = None else: cmake_prefix_path = _osp.join(_osp.dirname(_osp.dirname(__file__)), 'share', 'cmake')
[docs]def swap_tensors(t1, t2): """ This function swaps the content of the two Tensor objects. At a high level, this will make t1 have the content of t2 while preserving its identity. This will not work if t1 and t2 have different slots. """ # Ensure there are no weakrefs if weakref.getweakrefs(t1): raise RuntimeError("Cannot swap t1 because it has weakref associated with it") if weakref.getweakrefs(t2): raise RuntimeError("Cannot swap t2 because it has weakref associated with it") t1_slots = set(copyreg._slotnames(t1.__class__)) # type: ignore[attr-defined] t2_slots = set(copyreg._slotnames(t2.__class__)) # type: ignore[attr-defined] if t1_slots != t2_slots: raise RuntimeError("Cannot swap t1 and t2 if they have different slots") def swap_attr(name): tmp = getattr(t1, name) setattr(t1, name, (getattr(t2, name))) setattr(t2, name, tmp) # Swap the types # Note that this will fail if there are mismatched slots swap_attr("__class__") # Swap the dynamic attributes swap_attr("__dict__") # Swap the slots for slot in t1_slots: if hasattr(t1, slot) and hasattr(t2, slot): swap_attr(slot) elif hasattr(t1, slot): setattr(t2, slot, (getattr(t1, slot))) delattr(t1, slot) elif hasattr(t2, slot): setattr(t1, slot, (getattr(t2, slot))) delattr(t2, slot) # Swap the at::Tensor they point to torch._C._swap_tensor_impl(t1, t2)


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