AutogradContext {torch}

Class representing the context.

Description

Class representing the context.

Class representing the context.

Public fields

ptr

(Dev related) pointer to the context c++ object.

Active bindings

needs_input_grad

boolean listing arguments of forward and whether they require_grad.

saved_variables

list of objects that were saved for backward via save_for_backward.

Methods

Public methods

• AutogradContext$new()

• AutogradContext$save_for_backward()

• AutogradContext$mark_non_differentiable()

• AutogradContext$mark_dirty()

• AutogradContext$clone()

Method new()

(Dev related) Initializes the context. Not user related.

Usage

AutogradContext$new(
  ptr,
  env,
  argument_names = NULL,
  argument_needs_grad = NULL
)

Arguments

ptr

pointer to the c++ object

env

environment that encloses both forward and backward

argument_names

names of forward arguments

argument_needs_grad

whether each argument in forward needs grad.

Method save_for_backward()

Saves given objects for a future call to backward().

This should be called at most once, and only from inside the forward() method.

Later, saved objects can be accessed through the saved_variables attribute. Before returning them to the user, a check is made to ensure they weren’t used in any in-place operation that modified their content.

Arguments can also be any kind of R object.

Usage

AutogradContext$save_for_backward(...)

Arguments

...

any kind of R object that will be saved for the backward pass. It's common to pass named arguments.

Method mark_non_differentiable()

Marks outputs as non-differentiable.

This should be called at most once, only from inside the forward() method, and all arguments should be outputs.

This will mark outputs as not requiring gradients, increasing the efficiency of backward computation. You still need to accept a gradient for each output in backward(), but it’s always going to be a zero tensor with the same shape as the shape of a corresponding output.

This is used e.g. for indices returned from a max Function.

Usage

AutogradContext$mark_non_differentiable(...)

Arguments

...

non-differentiable outputs.

Method mark_dirty()

Marks given tensors as modified in an in-place operation.

This should be called at most once, only from inside the forward() method, and all arguments should be inputs.

Every tensor that’s been modified in-place in a call to forward() should be given to this function, to ensure correctness of our checks. It doesn’t matter whether the function is called before or after modification.

Usage

AutogradContext$mark_dirty(...)

Arguments

...

tensors that are modified in-place.

Method clone()

The objects of this class are cloneable with this method.

Usage

AutogradContext$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.