Class representing the context.
Class representing the context.
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
Method new()
(Dev related) Initializes the context. Not user related.
Usage
AutogradContext$new(
ptr,
env,
argument_names = NULL,
argument_needs_grad = NULL
)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.
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.
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.