org.bytedeco.pytorch

Class AutogradContext

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.pytorch.AutogradContext

All Implemented Interfaces:

AutoCloseable

@Namespace(value="torch::autograd")
 @NoOffset
 @Properties(inherit=torch.class)
public class AutogradContext
extends Pointer

Context to save information during forward that can be accessed in backward in custom autograd operations (see torch::autograd::Function for details).

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
AutogradContext()
AutogradContext(long size) Native array allocator.
AutogradContext(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
TensorImplSet	get_and_bump_dirty()
TensorImplSet	get_non_differentiable()
TensorVector	get_saved_variables() Get the list of variables that were saved in forward using save_for_backward().
AutogradContext	getPointer(long i)
void	mark_dirty(TensorVector inputs) Marks variables in the list as modified in an in-place operation.
void	mark_non_differentiable(TensorVector outputs) Marks outputs in the list as not requiring gradients.
boolean	needs_input_grad(long output_edge_index) Expose the Node's task_should_compute_output method to the cpp custom autograd Function as needs_input_grad.
AutogradContext	position(long position)
void	save_for_backward(TensorVector to_save) Saves the list of variables for a future call to backward.
void	set_materialize_grads(boolean value)

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, getDirectBufferAddress, getPointer, getPointer, getPointer, hashCode, interruptDeallocatorThread, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetAddress, offsetof, offsetof, parseBytes, physicalBytes, physicalBytesInaccurate, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, sizeof, toString, totalBytes, totalCount, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AutogradContext

public AutogradContext(Pointer p)

Pointer cast constructor. Invokes Pointer(Pointer).

AutogradContext

public AutogradContext(long size)

Native array allocator. Access with Pointer.position(long).

AutogradContext

public AutogradContext()

Method Detail

position

public AutogradContext position(long position)

Overrides:

position in class Pointer

getPointer

public AutogradContext getPointer(long i)

Overrides:

getPointer in class Pointer

save_for_backward

public void save_for_backward(@ByVal
                              TensorVector to_save)

Saves the list of variables for a future call to backward. This should be called at most once from inside of forward.

mark_dirty

public void mark_dirty(@Const @ByRef
                       TensorVector inputs)

Marks variables in the list as modified in an in-place operation. This should be called at most once from inside of forward and all arguments should be inputs.

mark_non_differentiable

public void mark_non_differentiable(@Const @ByRef
                                    TensorVector outputs)

Marks outputs in the list as not requiring gradients. This should be called at most once from inside of forward and all arguments should be outputs.

set_materialize_grads

public void set_materialize_grads(@Cast(value="bool")
                                  boolean value)

get_saved_variables

@ByVal
public TensorVector get_saved_variables()

Get the list of variables that were saved in forward using save_for_backward(). Before returning them to the user, a check is made to ensure that they were not modified by any in-place operations.

get_and_bump_dirty

@Const
 @ByRef
public TensorImplSet get_and_bump_dirty()

get_non_differentiable

@Const
 @ByRef
public TensorImplSet get_non_differentiable()

needs_input_grad

@Cast(value="bool")
public boolean needs_input_grad(@Cast(value="size_t")
                                                    long output_edge_index)

Expose the Node's task_should_compute_output method to the cpp custom autograd Function as needs_input_grad.