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