AutogradContext: Class representing the context.
In torch: Tensors and Neural Networks with 'GPU' Acceleration
AutogradContext R Documentation
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_gradboolean listing arguments of forward and whether they require_grad.
saved_variableslist of objects that were saved for backward via save_for_backward.
Methods
Public methods
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Method new()
(Dev related) Initializes the context. Not user related.
Usage
AutogradContext$new(
ptr,
env,
argument_names = NULL,
argument_needs_grad = NULL
)
Arguments
ptrpointer to the c++ object
envenvironment that encloses both forward and backward
argument_namesnames of forward arguments
argument_needs_gradwhether each argument in forward needs grad.
Method save_for_backward()
Saves given objects for a future call to backward().
This should be called at most once, and only from inside the forward()
method.
Later, saved objects can be accessed through the saved_variables attribute.
Before returning them to the user, a check is made to ensure they weren’t used
in any in-place operation that modified their content.
Arguments can also be any kind of R object.
Usage
AutogradContext$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$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
torch documentation built on June 7, 2023, 6:19 p.m.
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| AutogradContext | R Documentation |
Class representing the context.
Description
Class representing the context.
Class representing the context.
Public fields
ptr(Dev related) pointer to the context c++ object.
Active bindings
needs_input_gradboolean listing arguments of
forwardand whether they require_grad.saved_variableslist of objects that were saved for backward via
save_for_backward.
Methods
Public methods
Method new()
(Dev related) Initializes the context. Not user related.
Usage
AutogradContext$new( ptr, env, argument_names = NULL, argument_needs_grad = NULL )
Arguments
ptrpointer to the c++ object
envenvironment that encloses both forward and backward
argument_namesnames of forward arguments
argument_needs_gradwhether 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
deepWhether to make a deep clone.
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