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| from typing import Any | |
| import torch | |
| from torch.utils._contextlib import ( | |
| _DecoratorContextManager, | |
| _NoParamDecoratorContextManager, | |
| F, | |
| ) | |
| __all__ = [ | |
| "no_grad", | |
| "enable_grad", | |
| "set_grad_enabled", | |
| "inference_mode", | |
| "set_multithreading_enabled", | |
| ] | |
| class no_grad(_NoParamDecoratorContextManager): | |
| r"""Context-manager that disables gradient calculation. | |
| Disabling gradient calculation is useful for inference, when you are sure | |
| that you will not call :meth:`Tensor.backward()`. It will reduce memory | |
| consumption for computations that would otherwise have `requires_grad=True`. | |
| In this mode, the result of every computation will have | |
| `requires_grad=False`, even when the inputs have `requires_grad=True`. | |
| There is an exception! All factory functions, or functions that create | |
| a new Tensor and take a requires_grad kwarg, will NOT be affected by | |
| this mode. | |
| This context manager is thread local; it will not affect computation | |
| in other threads. | |
| Also functions as a decorator. | |
| .. note:: | |
| No-grad is one of several mechanisms that can enable or | |
| disable gradients locally see :ref:`locally-disable-grad-doc` for | |
| more information on how they compare. | |
| .. note:: | |
| This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`. | |
| If you want to disable forward AD for a computation, you can unpack | |
| your dual tensors. | |
| Example:: | |
| >>> # xdoctest: +SKIP | |
| >>> x = torch.tensor([1.], requires_grad=True) | |
| >>> with torch.no_grad(): | |
| ... y = x * 2 | |
| >>> y.requires_grad | |
| False | |
| >>> @torch.no_grad() | |
| ... def doubler(x): | |
| ... return x * 2 | |
| >>> z = doubler(x) | |
| >>> z.requires_grad | |
| False | |
| >>> @torch.no_grad | |
| ... def tripler(x): | |
| ... return x * 3 | |
| >>> z = tripler(x) | |
| >>> z.requires_grad | |
| False | |
| >>> # factory function exception | |
| >>> with torch.no_grad(): | |
| ... a = torch.nn.Parameter(torch.rand(10)) | |
| >>> a.requires_grad | |
| True | |
| """ | |
| def __init__(self) -> None: | |
| if not torch._jit_internal.is_scripting(): | |
| super().__init__() | |
| self.prev = False | |
| def __enter__(self) -> None: | |
| self.prev = torch.is_grad_enabled() | |
| torch.set_grad_enabled(False) | |
| def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: | |
| torch.set_grad_enabled(self.prev) | |
| class enable_grad(_NoParamDecoratorContextManager): | |
| r"""Context-manager that enables gradient calculation. | |
| Enables gradient calculation, if it has been disabled via :class:`~no_grad` | |
| or :class:`~set_grad_enabled`. | |
| This context manager is thread local; it will not affect computation | |
| in other threads. | |
| Also functions as a decorator. | |
| .. note:: | |
| enable_grad is one of several mechanisms that can enable or | |
| disable gradients locally see :ref:`locally-disable-grad-doc` for | |
| more information on how they compare. | |
| .. note:: | |
| This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`. | |
| Example:: | |
| >>> # xdoctest: +SKIP | |
| >>> x = torch.tensor([1.], requires_grad=True) | |
| >>> with torch.no_grad(): | |
| ... with torch.enable_grad(): | |
| ... y = x * 2 | |
| >>> y.requires_grad | |
| True | |
| >>> y.backward() | |
| >>> x.grad | |
| tensor([2.]) | |
| >>> @torch.enable_grad() | |
| ... def doubler(x): | |
| ... return x * 2 | |
| >>> with torch.no_grad(): | |
| ... z = doubler(x) | |
| >>> z.requires_grad | |
| True | |
| >>> @torch.enable_grad | |
| ... def tripler(x): | |
| ... return x * 3 | |
| >>> with torch.no_grad(): | |
| ... z = tripler(x) | |
| >>> z.requires_grad | |
| True | |
| """ | |
| def __enter__(self) -> None: | |
| self.prev = torch.is_grad_enabled() | |
| torch._C._set_grad_enabled(True) | |
| def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: | |
| torch._C._set_grad_enabled(self.prev) | |
| class set_grad_enabled(_DecoratorContextManager): | |
| r"""Context-manager that sets gradient calculation on or off. | |
| ``set_grad_enabled`` will enable or disable grads based on its argument :attr:`mode`. | |
| It can be used as a context-manager or as a function. | |
| This context manager is thread local; it will not affect computation | |
| in other threads. | |
| Args: | |
| mode (bool): Flag whether to enable grad (``True``), or disable | |
| (``False``). This can be used to conditionally enable | |
| gradients. | |
| .. note:: | |
| set_grad_enabled is one of several mechanisms that can enable or | |
| disable gradients locally see :ref:`locally-disable-grad-doc` for | |
| more information on how they compare. | |
| .. note:: | |
| This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`. | |
| Example:: | |
| >>> # xdoctest: +SKIP | |
| >>> x = torch.tensor([1.], requires_grad=True) | |
| >>> is_train = False | |
| >>> with torch.set_grad_enabled(is_train): | |
| ... y = x * 2 | |
| >>> y.requires_grad | |
| False | |
| >>> _ = torch.set_grad_enabled(True) | |
| >>> y = x * 2 | |
| >>> y.requires_grad | |
| True | |
| >>> _ = torch.set_grad_enabled(False) | |
| >>> y = x * 2 | |
| >>> y.requires_grad | |
| False | |
| """ | |
| def __init__(self, mode: bool) -> None: | |
| self.prev = torch.is_grad_enabled() | |
| self.mode = mode | |
| torch._C._set_grad_enabled(mode) | |
| def __call__(self, orig_func: F) -> F: | |
| torch._C._set_grad_enabled(self.prev) | |
| return super().__call__(orig_func) | |
| def __enter__(self) -> None: | |
| torch._C._set_grad_enabled(self.mode) | |
| def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: | |
| torch._C._set_grad_enabled(self.prev) | |
| def clone(self) -> "set_grad_enabled": | |
| r""" | |
| Create a copy of this class | |
| """ | |
| return self.__class__(self.mode) | |
| class inference_mode(_DecoratorContextManager): | |
| r"""Context-manager that enables or disables inference mode. | |
| InferenceMode is a new context manager analogous to :class:`~no_grad` | |
| to be used when you are certain your operations will have no interactions | |
| with autograd (e.g., model training). Code run under this mode gets better | |
| performance by disabling view tracking and version counter bumps. Note that | |
| unlike some other mechanisms that locally enable or disable grad, | |
| entering inference_mode also disables to :ref:`forward-mode AD <forward-mode-ad>`. | |
| This context manager is thread local; it will not affect computation | |
| in other threads. | |
| Also functions as a decorator. | |
| .. note:: | |
| Inference mode is one of several mechanisms that can enable or | |
| disable gradients locally see :ref:`locally-disable-grad-doc` for | |
| more information on how they compare. | |
| Args: | |
| mode (bool or function): Either a boolean flag whether to enable or | |
| disable inference mode or a Python function to decorate with | |
| inference mode enabled | |
| Example:: | |
| >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) | |
| >>> import torch | |
| >>> x = torch.ones(1, 2, 3, requires_grad=True) | |
| >>> with torch.inference_mode(): | |
| ... y = x * x | |
| >>> y.requires_grad | |
| False | |
| >>> # xdoctest: +SKIP("want string isnt quite right") | |
| >>> y._version | |
| Traceback (most recent call last): | |
| File "<stdin>", line 1, in <module> | |
| RuntimeError: Inference tensors do not track version counter. | |
| >>> @torch.inference_mode() | |
| ... def func(x): | |
| ... return x * x | |
| >>> out = func(x) | |
| >>> out.requires_grad | |
| False | |
| >>> @torch.inference_mode | |
| ... def doubler(x): | |
| ... return x * 2 | |
| >>> out = doubler(x) | |
| >>> out.requires_grad | |
| False | |
| """ | |
| def __init__(self, mode: bool = True) -> None: | |
| if not torch._jit_internal.is_scripting(): | |
| super().__init__() | |
| self.mode = mode | |
| def __new__(cls, mode=True): | |
| if isinstance(mode, bool): | |
| return super().__new__(cls) | |
| return cls()(mode) | |
| def __enter__(self) -> None: | |
| self._inference_mode_context = torch._C._InferenceMode(self.mode) | |
| self._inference_mode_context.__enter__() | |
| def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: | |
| self._inference_mode_context.__exit__(exc_type, exc_value, traceback) | |
| def clone(self) -> "inference_mode": | |
| r""" | |
| Create a copy of this class | |
| """ | |
| return self.__class__(self.mode) | |
| def _enter_inference_mode(mode): | |
| mode_context = torch._C._InferenceMode(mode) | |
| mode_context.__enter__() | |
| return mode_context | |
| def _exit_inference_mode(mode): | |
| mode.__exit__(None, None, None) | |
| class set_multithreading_enabled(_DecoratorContextManager): | |
| r"""Context-manager that sets multithreaded backwards on or off. | |
| ``set_multithreading_enabled`` will enable or disable multithreaded backwards based on its argument :attr:`mode`. | |
| It can be used as a context-manager or as a function. | |
| This context manager is thread local; it will not affect computation | |
| in other threads. | |
| Args: | |
| mode (bool): Flag whether to enable multithreaded backwards (``True``), or disable | |
| (``False``). | |
| .. note:: | |
| This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`. | |
| """ | |
| def __init__(self, mode: bool) -> None: | |
| self.prev = torch._C._is_multithreading_enabled() | |
| torch._C._set_multithreading_enabled(mode) | |
| self.mode = mode | |
| def __enter__(self) -> None: | |
| pass | |
| def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: | |
| torch._C._set_multithreading_enabled(self.prev) | |
| def clone(self) -> "set_multithreading_enabled": | |
| r""" | |
| Create a copy of this class | |
| """ | |
| return self.__class__(self.mode) | |
| class _force_original_view_tracking(_DecoratorContextManager): | |
| r"""Context-manager that sets whether or not to always enable view-replay in autograd. | |
| ``set_view_replay_enabled`` will enable or disable view-replay based on its argument :attr:`mode`. | |
| It can be used as a context-manager or as a function. | |
| This context manager is thread local; it will not affect computation | |
| in other threads. | |
| When a tensor view is mutated, the autograd engine needs to decide whether or not | |
| to regenerate the "updated view" by either replaying the chain of views from the updated base, | |
| or with a single call to as_strided. | |
| If set_view_replay_enabled is set to True, then autograd will always use view replay. | |
| Otherwise, it will fall back to its existing logic. | |
| Args: | |
| mode (bool): Flag whether to enable view-replay (``True``), or disable | |
| (``False``). | |
| """ | |
| def __init__(self, mode: bool) -> None: | |
| self.prev = torch._C._is_view_replay_enabled() | |
| torch._C._set_view_replay_enabled(mode) | |
| self.mode = mode | |
| def __enter__(self) -> None: | |
| pass | |
| def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: | |
| torch._C._set_view_replay_enabled(self.prev) | |
| def clone(self): | |
| return self.__class__(self.mode) | |
| class _unsafe_preserve_version_counter(_DecoratorContextManager): | |
| r"""DO NOT USE THIS UNLESS YOU KNOW EXACTLY WHAT YOU'RE DOING. | |
| This context manager can lead to arbitrary silent-correctness issues in any other part of your code | |
| (even the ones not touched directly by the context manager)! | |
| Ordinarily, autograd will track mutations to tensors by incrementing it's `._version` attribute. | |
| This is generally important for correctness, as for example, mutating a tensor that autograd has saved | |
| for the backwards pass can result in incorrect gradients, and autograd uses the version counter to detect | |
| and error out in this situation. | |
| However, there are rare instances where it might be useful to hide mutations from autograd. For example: | |
| if a tensor is very large, and you'd like to free its memory by storing it elsewhere, and re-populate | |
| the tensor right before it is needed by autograd. | |
| Args: | |
| tensor (torch.Tensor): the tensor in question, that you would like to preserve the version counter of. | |
| .. note:: | |
| This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`. | |
| """ | |
| def __init__(self, tensor: torch.Tensor) -> None: | |
| self.tensor = tensor | |
| self.prev_version = tensor._version | |
| def __enter__(self) -> None: | |
| pass | |
| def __exit__(self, *args) -> None: | |
| torch._C._autograd._unsafe_set_version_counter(self.tensor, self.prev_version) | |