curobo.rollout.cost.bound_cost module
- class BoundCostType(value)
Bases:
Enum
An enumeration.
- POSITION = 0
- BOUNDS = 1
- BOUNDS_SMOOTH = 2
- class BoundCostConfig(
- weight: torch.Tensor | float | List[float],
- tensor_args: curobo.types.base.TensorDeviceType = None,
- distance_threshold: float = 0.0,
- classify: bool = False,
- terminal: bool = False,
- run_weight: float | None = None,
- dof: int = 7,
- vec_weight: torch.Tensor | List[float] | float | NoneType = None,
- max_value: float | None = None,
- hinge_value: float | None = None,
- vec_convergence: List[float] | None = None,
- threshold_value: float | None = None,
- return_loss: bool = False,
- joint_limits: curobo.cuda_robot_model.types.JointLimits | None = None,
- smooth_weight: List[float] | None = None,
- run_weight_velocity: float = 0.0,
- run_weight_acceleration: float = 0.0,
- run_weight_jerk: float = 0.0,
- cspace_distance_weight: torch.Tensor | None = None,
- cost_type: curobo.rollout.cost.bound_cost.BoundCostType | None = None,
- activation_distance: torch.Tensor | float = 0.0,
- state_finite_difference_mode: str = 'BACKWARD',
- null_space_weight: List[float] | None = None,
Bases:
CostConfig
- joint_limits: JointLimits | None = None
- cost_type: BoundCostType | None = None
- set_bounds(
- bounds: JointLimits,
- teleport_mode: bool = False,
- tensor_args: TensorDeviceType = None
- update_vec_weight(
- vec_weight,
- vec_weight: torch.Tensor | List[float] | float | None = None
- weight: torch.Tensor | float | List[float]
- class BoundCost(
- config: BoundCostConfig,
Bases:
CostBase
,BoundCostConfig
Initialize class
- Parameters:
config (Optional[CostConfig], optional) – To initialize this class directly, pass a config.
class (If this is a base) –
CostConfig. (it's assumed that you will initialize the child class with) –
None. (Defaults to) –
- update_batch_size(
- batch,
- horizon,
- dof,
- forward(
- state_batch: JointState,
- retract_config: Tensor | None = None,
- retract_idx: Tensor | None = None,
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- T_destination = ~T_destination
- _apply(fn, recurse=True)
- _call_impl(*args, **kwargs)
- _get_backward_hooks()
Return the backward hooks for use in the call function.
It returns two lists, one with the full backward hooks and one with the non-full backward hooks.
- _get_backward_pre_hooks()
- _get_name()
- _init_post_config()
- _load_from_state_dict(
- state_dict,
- prefix,
- local_metadata,
- strict,
- missing_keys,
- unexpected_keys,
- error_msgs,
Copy parameters and buffers from
state_dict
into only this module, but not its descendants.This is called on every submodule in
load_state_dict
. Metadata saved for this module in inputstate_dict
is provided aslocal_metadata
. For state dicts without metadata,local_metadata
is empty. Subclasses can achieve class-specific backward compatible loading using the version number at local_metadata.get(“version”, None). Additionally,local_metadata
can also contain the key assign_to_params_buffers that indicates whether keys should be assigned their corresponding tensor in the state_dict.Note
state_dict
is not the same object as the inputstate_dict
toload_state_dict
. So it can be modified.- Parameters:
state_dict (dict) – a dict containing parameters and persistent buffers.
prefix (str) – the prefix for parameters and buffers used in this module
local_metadata (dict) – a dict containing the metadata for this module. See
strict (bool) – whether to strictly enforce that the keys in
state_dict
withprefix
match the names of parameters and buffers in this modulemissing_keys (list of str) – if
strict=True
, add missing keys to this listunexpected_keys (list of str) – if
strict=True
, add unexpected keys to this listerror_msgs (list of str) – error messages should be added to this list, and will be reported together in
load_state_dict
- _maybe_warn_non_full_backward_hook(
- inputs,
- result,
- grad_fn,
- _named_members(
- get_members_fn,
- prefix='',
- recurse=True,
- remove_duplicate: bool = True,
Help yield various names + members of modules.
- _register_load_state_dict_pre_hook(
- hook,
- with_module=False,
Register a pre-hook for the
load_state_dict
method.These hooks will be called with arguments: state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, before loading state_dict into self. These arguments are exactly the same as those of _load_from_state_dict.
If
with_module
isTrue
, then the first argument to the hook is an instance of the module.- Parameters:
hook (Callable) – Callable hook that will be invoked before loading the state dict.
with_module (bool, optional) – Whether or not to pass the module instance to the hook as the first parameter.
- _register_state_dict_hook(
- hook,
Register a state-dict hook.
These hooks will be called with arguments: self, state_dict, prefix, local_metadata, after the state_dict of self is set. Note that only parameters and buffers of self or its children are guaranteed to exist in state_dict. The hooks may modify state_dict inplace or return a new one.
- _replicate_for_data_parallel()
- _save_to_state_dict(
- destination,
- prefix,
- keep_vars,
Save module state to the destination dictionary.
The destination dictionary will contain the state of the module, but not its descendants. This is called on every submodule in
state_dict
.In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic.
- _slow_forward(
- *input,
- **kwargs,
- _version: int = 1
This allows better BC support for
load_state_dict
. Instate_dict
, the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See_load_from_state_dict
on how to use this information in loading.If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.
- _wrapped_call_impl(
- *args,
- **kwargs,
- activation_distance: torch.Tensor | float = 0.0
- add_module( ) None
Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters:
name (str) – name of the child module. The child module can be accessed from this module using the given name
module (Module) – child module to be added to the module.
- apply( ) T
Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Parameters:
fn (
Module
-> None) – function to be applied to each submodule- Returns:
self
- Return type:
Module
Example:
>>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )
- bfloat16() T
Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns:
self
- Return type:
Module
- buffers(
- recurse: bool = True,
Return an iterator over module buffers.
- Parameters:
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
- Yields:
torch.Tensor – module buffer
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L)
- children() Iterator[Module]
Return an iterator over immediate children modules.
- Yields:
Module – a child module
- compile(*args, **kwargs)
Compile this Module’s forward using
torch.compile
.This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile
.See
torch.compile
for details on the arguments for this function.
- cost_type: BoundCostType | None = None
- cpu() T
Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns:
self
- Return type:
Module
- cuda( ) T
Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.
Note
This method modifies the module in-place.
- Parameters:
device (int, optional) – if specified, all parameters will be copied to that device
- Returns:
self
- Return type:
Module
- disable_cost()
- double() T
Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns:
self
- Return type:
Module
- enable_cost()
- property enabled
- eval() T
Set the module in evaluation mode.
This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns:
self
- Return type:
Module
- extra_repr() str
Set the extra representation of the module.
To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.
- float() T
Casts all floating point parameters and buffers to
float
datatype.Note
This method modifies the module in-place.
- Returns:
self
- Return type:
Module
- get_buffer(
- target: str,
Return the buffer given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters:
target – The fully-qualified string name of the buffer to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns:
The buffer referenced by
target
- Return type:
- Raises:
AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
- get_extra_state() Any
Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
set_extra_state
for your module if you need to store extra state. This function is called when building the module’s state_dict().Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.
- Returns:
Any extra state to store in the module’s state_dict
- Return type:
- get_parameter(
- target: str,
Return the parameter given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters:
target – The fully-qualified string name of the Parameter to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns:
The Parameter referenced by
target
- Return type:
torch.nn.Parameter
- Raises:
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Parameter
- get_submodule(
- target: str,
Return the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this:A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) )
(The diagram shows an
nn.Module
A
.A
has a nested submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To check whether or not we have the
linear
submodule, we would callget_submodule("net_b.linear")
. To check whether we have theconv
submodule, we would callget_submodule("net_b.net_c.conv")
.The runtime of
get_submodule
is bounded by the degree of module nesting intarget
. A query againstnamed_modules
achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists,get_submodule
should always be used.- Parameters:
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
- Returns:
The submodule referenced by
target
- Return type:
- Raises:
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
- half() T
Casts all floating point parameters and buffers to
half
datatype.Note
This method modifies the module in-place.
- Returns:
self
- Return type:
Module
- ipu( ) T
Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.
Note
This method modifies the module in-place.
- Parameters:
device (int, optional) – if specified, all parameters will be copied to that device
- Returns:
self
- Return type:
Module
- joint_limits: JointLimits | None = None
- load_state_dict( )
Copy parameters and buffers from
state_dict
into this module and its descendants.If
strict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict
function.Warning
If
assign
isTrue
the optimizer must be created after the call toload_state_dict
.- Parameters:
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict
function. Default:True
assign (bool, optional) – whether to assign items in the state dictionary to their corresponding keys in the module instead of copying them inplace into the module’s current parameters and buffers. When
False
, the properties of the tensors in the current module are preserved while whenTrue
, the properties of the Tensors in the state dict are preserved. Default:False
- Returns:
missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys
- Return type:
NamedTuple
withmissing_keys
andunexpected_keys
fields
Note
If a parameter or buffer is registered as
None
and its corresponding key exists instate_dict
,load_state_dict
will raise aRuntimeError
.
- modules() Iterator[Module]
Return an iterator over all modules in the network.
- Yields:
Module – a module in the network
Note
Duplicate modules are returned only once. In the following example,
l
will be returned only once.Example:
>>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): ... print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True)
- named_buffers( ) Iterator[Tuple[str, Tensor]]
Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters:
prefix (str) – prefix to prepend to all buffer names.
recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.
remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.
- Yields:
(str, torch.Tensor) – Tuple containing the name and buffer
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size())
- named_children() Iterator[Tuple[str, Module]]
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
- Yields:
(str, Module) – Tuple containing a name and child module
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module)
- named_modules( )
Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Parameters:
memo – a memo to store the set of modules already added to the result
prefix – a prefix that will be added to the name of the module
remove_duplicate – whether to remove the duplicated module instances in the result or not
- Yields:
(str, Module) – Tuple of name and module
Note
Duplicate modules are returned only once. In the following example,
l
will be returned only once.Example:
>>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): ... print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
- named_parameters( ) Iterator[Tuple[str, Parameter]]
Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters:
prefix (str) – prefix to prepend to all parameter names.
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.
- Yields:
(str, Parameter) – Tuple containing the name and parameter
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size())
- parameters(
- recurse: bool = True,
Return an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters:
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
- Yields:
Parameter – module parameter
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for param in model.parameters(): >>> print(type(param), param.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L)
- register_backward_hook(
- hook: Callable[[Module, Tuple[Tensor, ...] | Tensor, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor],
Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook
and the behavior of this function will change in future versions.- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemovableHandle
- register_buffer( ) None
Add a buffer to the module.
This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s
running_mean
is not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters:
name (str) – name of the buffer. The buffer can be accessed from this module using the given name
tensor (Tensor or None) – buffer to be registered. If
None
, then operations that run on buffers, such ascuda
, are ignored. IfNone
, the buffer is not included in the module’sstate_dict
.persistent (bool) – whether the buffer is part of this module’s
state_dict
.
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> self.register_buffer('running_mean', torch.zeros(num_features))
- register_forward_hook(
- hook: Callable[[T, Tuple[Any, ...], Any], Any | None] | Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Any | None],
- *,
- prepend: bool = False,
- with_kwargs: bool = False,
- always_call: bool = False,
Register a forward hook on the module.
The hook will be called every time after
forward
has computed an output.If
with_kwargs
isFalse
or not specified, the input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to theforward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward
is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargs
isTrue
, the forward hook will be passed thekwargs
given to the forward function and be expected to return the output possibly modified. The hook should have the following signature:hook(module, args, kwargs, output) -> None or modified output
- Parameters:
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If
True
, the providedhook
will be fired before all existingforward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward
hooks on thistorch.nn.modules.Module
. Note that globalforward
hooks registered withregister_module_forward_hook
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If
True
, thehook
will be passed the kwargs given to the forward function. Default:False
always_call (bool) – If
True
thehook
will be run regardless of whether an exception is raised while calling the Module. Default:False
- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemovableHandle
- register_forward_pre_hook(
- hook: Callable[[T, Tuple[Any, ...]], Any | None] | Callable[[T, Tuple[Any, ...], Dict[str, Any]], Tuple[Any, Dict[str, Any]] | None],
- *,
- prepend: bool = False,
- with_kwargs: bool = False,
Register a forward pre-hook on the module.
The hook will be called every time before
forward
is invoked.If
with_kwargs
is false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to theforward
. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature:hook(module, args) -> None or modified input
If
with_kwargs
is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
- Parameters:
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingforward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward_pre
hooks on thistorch.nn.modules.Module
. Note that globalforward_pre
hooks registered withregister_module_forward_pre_hook
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If true, the
hook
will be passed the kwargs given to the forward function. Default:False
- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemovableHandle
- register_full_backward_hook(
- hook: Callable[[Module, Tuple[Tensor, ...] | Tensor, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor],
- prepend: bool = False,
Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The
grad_input
andgrad_output
are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_input
andgrad_output
will beNone
for all non-Tensor arguments.For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module’s forward function.
Warning
Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.
- Parameters:
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward
hooks on thistorch.nn.modules.Module
. Note that globalbackward
hooks registered withregister_module_full_backward_hook
will fire before all hooks registered by this method.
- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemovableHandle
- register_full_backward_pre_hook(
- hook: Callable[[Module, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor],
- prepend: bool = False,
Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed. The hook should have the following signature:
hook(module, grad_output) -> tuple[Tensor] or None
The
grad_output
is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place ofgrad_output
in subsequent computations. Entries ingrad_output
will beNone
for all non-Tensor arguments.For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module’s forward function.
Warning
Modifying inputs inplace is not allowed when using backward hooks and will raise an error.
- Parameters:
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Note that globalbackward_pre
hooks registered withregister_module_full_backward_pre_hook
will fire before all hooks registered by this method.
- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemovableHandle
- register_load_state_dict_post_hook(
- hook,
Register a post hook to be run after module’s
load_state_dict
is called.- It should have the following signature::
hook(module, incompatible_keys) -> None
The
module
argument is the current module that this hook is registered on, and theincompatible_keys
argument is aNamedTuple
consisting of attributesmissing_keys
andunexpected_keys
.missing_keys
is alist
ofstr
containing the missing keys andunexpected_keys
is alist
ofstr
containing the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict
withstrict=True
are affected by modifications the hook makes tomissing_keys
orunexpected_keys
, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True
, and clearing out both missing and unexpected keys will avoid an error.- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemovableHandle
- register_parameter( ) None
Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters:
name (str) – name of the parameter. The parameter can be accessed from this module using the given name
param (Parameter or None) – parameter to be added to the module. If
None
, then operations that run on parameters, such ascuda
, are ignored. IfNone
, the parameter is not included in the module’sstate_dict
.
- register_state_dict_pre_hook(
- hook,
Register a pre-hook for the
load_state_dict
method.These hooks will be called with arguments:
self
,prefix
, andkeep_vars
before callingstate_dict
onself
. The registered hooks can be used to perform pre-processing before thestate_dict
call is made.
- requires_grad_(
- requires_grad: bool = True,
Change if autograd should record operations on parameters in this module.
This method sets the parameters’
requires_grad
attributes in-place.This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).
See Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Parameters:
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns:
self
- Return type:
Module
- set_bounds(
- bounds: JointLimits,
- teleport_mode: bool = False,
- set_extra_state(
- state: Any,
Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict
to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state
for your module if you need to store extra state within its state_dict.- Parameters:
state (dict) – Extra state from the state_dict
- state_dict(
- *args,
- destination=None,
- prefix='',
- keep_vars=False,
Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to
None
are not included.Note
The returned object is a shallow copy. It contains references to the module’s parameters and buffers.
Warning
Currently
state_dict()
also accepts positional arguments fordestination
,prefix
andkeep_vars
in order. However, this is being deprecated and keyword arguments will be enforced in future releases.Warning
Please avoid the use of argument
destination
as it is not designed for end-users.- Parameters:
destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an
OrderedDict
will be created and returned. Default:None
.prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default:
''
.keep_vars (bool, optional) – by default the
Tensor
s returned in the state dict are detached from autograd. If it’s set toTrue
, detaching will not be performed. Default:False
.
- Returns:
a dictionary containing a whole state of the module
- Return type:
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
- tensor_args: TensorDeviceType = None
- to(*args, **kwargs)
Move and/or cast the parameters and buffers.
This can be called as
- to(
- device=None,
- dtype=None,
- non_blocking=False,
- to(dtype, non_blocking=False)
- to(tensor, non_blocking=False)
- to(memory_format=torch.channels_last)
Its signature is similar to
torch.Tensor.to
, but only accepts floating point or complexdtype
s. In addition, this method will only cast the floating point or complex parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_blocking
is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.See below for examples.
Note
This method modifies the module in-place.
- Parameters:
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point or complex dtype of the parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns:
self
- Return type:
Module
Examples:
>>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> linear = nn.Linear(2, 2) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]]) >>> linear.to(torch.double) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]], dtype=torch.float64) >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1) >>> gpu1 = torch.device("cuda:1") >>> linear.to(gpu1, dtype=torch.half, non_blocking=True) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1') >>> cpu = torch.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16) >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble) >>> linear.weight Parameter containing: tensor([[ 0.3741+0.j, 0.2382+0.j], [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128) >>> linear(torch.ones(3, 2, dtype=torch.cdouble)) tensor([[0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
- to_empty( ) T
Move the parameters and buffers to the specified device without copying storage.
- Parameters:
device (
torch.device
) – The desired device of the parameters and buffers in this module.recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.
- Returns:
self
- Return type:
Module
- train(
- mode: bool = True,
Set the module in training mode.
This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.
Dropout
,BatchNorm
, etc.- Parameters:
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns:
self
- Return type:
Module
- type( ) T
Casts all parameters and buffers to
dst_type
.Note
This method modifies the module in-place.
- Parameters:
dst_type (type or string) – the desired type
- Returns:
self
- Return type:
Module
- update_vec_weight(vec_weight)
- vec_weight: torch.Tensor | List[float] | float | None = None
- xpu( ) T
Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.
Note
This method modifies the module in-place.
- Parameters:
device (int, optional) – if specified, all parameters will be copied to that device
- Returns:
self
- Return type:
Module
- zero_grad(
- set_to_none: bool = True,
Reset gradients of all model parameters.
See similar function under
torch.optim.Optimizer
for more context.- Parameters:
set_to_none (bool) – instead of setting to zero, set the grads to None. See
torch.optim.Optimizer.zero_grad
for details.
- weight: torch.Tensor | float | List[float]
- class WarpBoundSmoothFunction(*args, **kwargs)
Bases:
Function
- static forward(
- ctx,
- pos,
- vel,
- acc,
- jerk,
- retract_config,
- retract_idx,
- p_b,
- v_b,
- a_b,
- j_b,
- weight,
- activation_distance,
- smooth_weight,
- cspace_weight,
- null_space_weight,
- vec_weight,
- run_weight_vel,
- run_weight_acc,
- run_weight_jerk,
- out_cost,
- out_gp,
- out_gv,
- out_ga,
- out_gj,
This function is to be overridden by all subclasses. There are two ways to define forward:
Usage 1 (Combined forward and ctx):
@staticmethod def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any: pass
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See Combined or separate forward() and setup_context() for more details
Usage 2 (Separate forward and ctx):
@staticmethod def forward(*args: Any, **kwargs: Any) -> Any: pass @staticmethod def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None: pass
The forward no longer accepts a ctx argument.
Instead, you must also override the
torch.autograd.Function.setup_context
staticmethod to handle setting up thectx
object.output
is the output of the forward,inputs
are a Tuple of inputs to the forward.See Extending torch.autograd for more details
The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with
ctx.save_for_backward
if they are intended to be used inbackward
(equivalently,vjp
) orctx.save_for_forward
if they are intended to be used for injvp
.
- static backward(
- ctx,
- grad_out_cost,
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- _backward_cls
alias of
WarpBoundSmoothFunctionBackward
- _get_compiled_autograd_symints()
- _is_compiled_autograd_tracing()
- _materialize_non_diff_grads
- _raw_saved_tensors
- static _register_hook(
- backward_hooks,
- hook,
- _register_hook_dict()
- _sequence_nr()
- classmethod apply(
- *args,
- **kwargs,
- dirty_tensors
- generate_vmap_rule = False
- is_traceable = False
Bool that specifies if PyTorch should attempt to autogenerate
torch.vmap
support for this autograd.Function. You may set this to True only if this autograd.Function’s forward, backward, and jvp (if they exist) are written using PyTorch operations; otherwise, please overridetorch.autograd.Function.vmap
to add support fortorch.vmap
.Please see Extending torch.func with autograd.Function for more details.
- static jvp( ) Any
Defines a formula for differentiating the operation with forward mode automatic differentiation. This function is to be overridden by all subclasses. It must accept a context
ctx
as the first argument, followed by as many inputs as theforward
got (None will be passed in for non tensor inputs of the forward function), and it should return as many tensors as there were outputs toforward
. Each argument is the gradient w.r.t the given input, and each returned value should be the gradient w.r.t. the corresponding output. If an output is not a Tensor or the function is not differentiable with respect to that output, you can just pass None as a gradient for that input.You can use the
ctx
object to pass any value from the forward to this functions.
- mark_dirty(
- *args: Tensor,
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.- Examples::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Inplace(Function): >>> @staticmethod >>> def forward(ctx, x): >>> x_npy = x.numpy() # x_npy shares storage with x >>> x_npy += 1 >>> ctx.mark_dirty(x) >>> return x >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_output): >>> return grad_output >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone() >>> b = a * a >>> Inplace.apply(a) # This would lead to wrong gradients! >>> # but the engine would not know unless we mark_dirty >>> # xdoctest: +SKIP >>> b.backward() # RuntimeError: one of the variables needed for gradient >>> # computation has been modified by an inplace operation
- mark_non_differentiable(
- *args: Tensor,
Marks outputs as non-differentiable.
This should be called at most once, only from inside the
forward
method, and all arguments should be tensor 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 sort. See example::
>>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> sorted, idx = x.sort() >>> ctx.mark_non_differentiable(idx) >>> ctx.save_for_backward(x, idx) >>> return sorted, idx >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): # still need to accept g2 >>> x, idx = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> grad_input.index_add_(0, idx, g1) >>> return grad_input
- *pairs,
- materialize_grads
- maybe_clear_saved_tensors()
- metadata
- name()
- needs_input_grad
- next_functions
- non_differentiable
- register_hook()
- register_prehook()
- requires_grad
- save_for_backward(
- *tensors: Tensor,
Saves given tensors for a future call to
backward
.save_for_backward
should be called at most once, only from inside theforward
method, and only with tensors.All tensors intended to be used in the backward pass should be saved with
save_for_backward
(as opposed to directly onctx
) to prevent incorrect gradients and memory leaks, and enable the application of saved tensor hooks. Seetorch.autograd.graph.saved_tensors_hooks
.Note that if intermediary tensors, tensors that are neither inputs nor outputs of
forward
, are saved for backward, your custom Function may not support double backward. Custom Functions that do not support double backward should decorate theirbackward
method with@once_differentiable
so that performing double backward raises an error. If you’d like to support double backward, you can either recompute intermediaries based on the inputs during backward or return the intermediaries as the outputs of the custom Function. See the double backward tutorial for more details.In
backward
, saved tensors can be accessed through thesaved_tensors
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
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> w = x * z >>> out = x * y + y * z + w * y >>> ctx.save_for_backward(x, y, w, out) >>> ctx.z = z # z is not a tensor >>> return out >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_out): >>> x, y, w, out = ctx.saved_tensors >>> z = ctx.z >>> gx = grad_out * (y + y * z) >>> gy = grad_out * (x + z + w) >>> gz = None >>> return gx, gy, gz >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> d = Func.apply(a, b, c)
- save_for_forward(
- *tensors: Tensor,
Saves given tensors for a future call to
jvp
.save_for_forward
should be only called once, from inside theforward
method, and only be called with tensors.In
jvp
, saved objects can be accessed through thesaved_tensors
attribute.Arguments can also be
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +SKIP >>> class Func(torch.autograd.Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> ctx.save_for_backward(x, y) >>> ctx.save_for_forward(x, y) >>> ctx.z = z >>> return x * y * z >>> >>> @staticmethod >>> def jvp(ctx, x_t, y_t, _): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * (y * x_t + x * y_t) >>> >>> @staticmethod >>> def vjp(ctx, grad_out): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * grad_out * y, z * grad_out * x, None >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> t = torch.tensor(1., dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> >>> with fwAD.dual_level(): >>> a_dual = fwAD.make_dual(a, t) >>> d = Func.apply(a_dual, b, c)
- saved_for_forward
- saved_tensors
- saved_variables
- set_materialize_grads(
- value: bool,
Sets whether to materialize grad tensors. Default is
True
.This should be called only from inside the
forward
methodIf
True
, undefined grad tensors will be expanded to tensors full of zeros prior to calling thebackward
andjvp
methods.- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class SimpleFunc(Function): >>> @staticmethod >>> def forward(ctx, x): >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> return g1 + g2 # No check for None necessary >>> >>> # We modify SimpleFunc to handle non-materialized grad outputs >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> ctx.set_materialize_grads(False) >>> ctx.save_for_backward(x) >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> x, = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> if g1 is not None: # We must check for None now >>> grad_input += g1 >>> if g2 is not None: >>> grad_input += g2 >>> return grad_input >>> >>> a = torch.tensor(1., requires_grad=True) >>> b, _ = Func.apply(a) # induces g2 to be undefined
- static setup_context( ) Any
There are two ways to define the forward pass of an autograd.Function.
Either:
Override forward with the signature forward(ctx, *args, **kwargs).
setup_context
is not overridden. Setting up the ctx for backward happens inside theforward
.Override forward with the signature forward(*args, **kwargs) and override
setup_context
. Setting up the ctx for backward happens insidesetup_context
(as opposed to inside theforward
)
See
torch.autograd.Function.forward
and Extending torch.autograd for more details.
- to_save
- static vjp( ) Any
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- static vmap(
- info,
- in_dims,
- *args,
Defines a rule for the behavior of this autograd.Function underneath
torch.vmap
. For atorch.autograd.Function
to supporttorch.vmap
, you must either override this staticmethod, or setgenerate_vmap_rule
toTrue
(you may not do both).If you choose to override this staticmethod: it must accept
an
info
object as the first argument.info.batch_size
specifies the size of the dimension being vmapped over, whileinfo.randomness
is the randomness option passed totorch.vmap
.an
in_dims
tuple as the second argument. For each arg inargs
,in_dims
has a correspondingOptional[int]
. It isNone
if the arg is not a Tensor or if the arg is not being vmapped over, otherwise, it is an integer specifying what dimension of the Tensor is being vmapped over.*args
, which is the same as the args toforward
.
The return of the vmap staticmethod is a tuple of
(output, out_dims)
. Similar toin_dims
,out_dims
should be of the same structure asoutput
and contain oneout_dim
per output that specifies if the output has the vmapped dimension and what index it is in.Please see Extending torch.func with autograd.Function for more details.
- class WarpBoundFunction(*args, **kwargs)
Bases:
Function
- static forward(
- ctx,
- pos,
- vel,
- acc,
- jerk,
- retract_config,
- retract_idx,
- p_b,
- v_b,
- a_b,
- j_b,
- weight,
- activation_distance,
- null_space_weight,
- vec_weight,
- out_cost,
- out_gp,
- out_gv,
- out_ga,
- out_gj,
This function is to be overridden by all subclasses. There are two ways to define forward:
Usage 1 (Combined forward and ctx):
@staticmethod def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any: pass
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See Combined or separate forward() and setup_context() for more details
Usage 2 (Separate forward and ctx):
@staticmethod def forward(*args: Any, **kwargs: Any) -> Any: pass @staticmethod def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None: pass
The forward no longer accepts a ctx argument.
Instead, you must also override the
torch.autograd.Function.setup_context
staticmethod to handle setting up thectx
object.output
is the output of the forward,inputs
are a Tuple of inputs to the forward.See Extending torch.autograd for more details
The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with
ctx.save_for_backward
if they are intended to be used inbackward
(equivalently,vjp
) orctx.save_for_forward
if they are intended to be used for injvp
.
- static backward(
- ctx,
- grad_out_cost,
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- _backward_cls
alias of
WarpBoundFunctionBackward
- _get_compiled_autograd_symints()
- _is_compiled_autograd_tracing()
- _materialize_non_diff_grads
- _raw_saved_tensors
- static _register_hook(
- backward_hooks,
- hook,
- _register_hook_dict()
- _sequence_nr()
- classmethod apply(*args, **kwargs)
- dirty_tensors
- generate_vmap_rule = False
- is_traceable = False
Bool that specifies if PyTorch should attempt to autogenerate
torch.vmap
support for this autograd.Function. You may set this to True only if this autograd.Function’s forward, backward, and jvp (if they exist) are written using PyTorch operations; otherwise, please overridetorch.autograd.Function.vmap
to add support fortorch.vmap
.Please see Extending torch.func with autograd.Function for more details.
- static jvp( ) Any
Defines a formula for differentiating the operation with forward mode automatic differentiation. This function is to be overridden by all subclasses. It must accept a context
ctx
as the first argument, followed by as many inputs as theforward
got (None will be passed in for non tensor inputs of the forward function), and it should return as many tensors as there were outputs toforward
. Each argument is the gradient w.r.t the given input, and each returned value should be the gradient w.r.t. the corresponding output. If an output is not a Tensor or the function is not differentiable with respect to that output, you can just pass None as a gradient for that input.You can use the
ctx
object to pass any value from the forward to this functions.
- mark_dirty(
- *args: Tensor,
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.- Examples::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Inplace(Function): >>> @staticmethod >>> def forward(ctx, x): >>> x_npy = x.numpy() # x_npy shares storage with x >>> x_npy += 1 >>> ctx.mark_dirty(x) >>> return x >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_output): >>> return grad_output >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone() >>> b = a * a >>> Inplace.apply(a) # This would lead to wrong gradients! >>> # but the engine would not know unless we mark_dirty >>> # xdoctest: +SKIP >>> b.backward() # RuntimeError: one of the variables needed for gradient >>> # computation has been modified by an inplace operation
- mark_non_differentiable(
- *args: Tensor,
Marks outputs as non-differentiable.
This should be called at most once, only from inside the
forward
method, and all arguments should be tensor 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 sort. See example::
>>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> sorted, idx = x.sort() >>> ctx.mark_non_differentiable(idx) >>> ctx.save_for_backward(x, idx) >>> return sorted, idx >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): # still need to accept g2 >>> x, idx = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> grad_input.index_add_(0, idx, g1) >>> return grad_input
- *pairs,
- materialize_grads
- maybe_clear_saved_tensors()
- metadata
- name()
- needs_input_grad
- next_functions
- non_differentiable
- register_hook()
- register_prehook()
- requires_grad
- save_for_backward(
- *tensors: Tensor,
Saves given tensors for a future call to
backward
.save_for_backward
should be called at most once, only from inside theforward
method, and only with tensors.All tensors intended to be used in the backward pass should be saved with
save_for_backward
(as opposed to directly onctx
) to prevent incorrect gradients and memory leaks, and enable the application of saved tensor hooks. Seetorch.autograd.graph.saved_tensors_hooks
.Note that if intermediary tensors, tensors that are neither inputs nor outputs of
forward
, are saved for backward, your custom Function may not support double backward. Custom Functions that do not support double backward should decorate theirbackward
method with@once_differentiable
so that performing double backward raises an error. If you’d like to support double backward, you can either recompute intermediaries based on the inputs during backward or return the intermediaries as the outputs of the custom Function. See the double backward tutorial for more details.In
backward
, saved tensors can be accessed through thesaved_tensors
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
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> w = x * z >>> out = x * y + y * z + w * y >>> ctx.save_for_backward(x, y, w, out) >>> ctx.z = z # z is not a tensor >>> return out >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_out): >>> x, y, w, out = ctx.saved_tensors >>> z = ctx.z >>> gx = grad_out * (y + y * z) >>> gy = grad_out * (x + z + w) >>> gz = None >>> return gx, gy, gz >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> d = Func.apply(a, b, c)
- save_for_forward(
- *tensors: Tensor,
Saves given tensors for a future call to
jvp
.save_for_forward
should be only called once, from inside theforward
method, and only be called with tensors.In
jvp
, saved objects can be accessed through thesaved_tensors
attribute.Arguments can also be
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +SKIP >>> class Func(torch.autograd.Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> ctx.save_for_backward(x, y) >>> ctx.save_for_forward(x, y) >>> ctx.z = z >>> return x * y * z >>> >>> @staticmethod >>> def jvp(ctx, x_t, y_t, _): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * (y * x_t + x * y_t) >>> >>> @staticmethod >>> def vjp(ctx, grad_out): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * grad_out * y, z * grad_out * x, None >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> t = torch.tensor(1., dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> >>> with fwAD.dual_level(): >>> a_dual = fwAD.make_dual(a, t) >>> d = Func.apply(a_dual, b, c)
- saved_for_forward
- saved_tensors
- saved_variables
- set_materialize_grads(
- value: bool,
Sets whether to materialize grad tensors. Default is
True
.This should be called only from inside the
forward
methodIf
True
, undefined grad tensors will be expanded to tensors full of zeros prior to calling thebackward
andjvp
methods.- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class SimpleFunc(Function): >>> @staticmethod >>> def forward(ctx, x): >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> return g1 + g2 # No check for None necessary >>> >>> # We modify SimpleFunc to handle non-materialized grad outputs >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> ctx.set_materialize_grads(False) >>> ctx.save_for_backward(x) >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> x, = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> if g1 is not None: # We must check for None now >>> grad_input += g1 >>> if g2 is not None: >>> grad_input += g2 >>> return grad_input >>> >>> a = torch.tensor(1., requires_grad=True) >>> b, _ = Func.apply(a) # induces g2 to be undefined
- static setup_context( ) Any
There are two ways to define the forward pass of an autograd.Function.
Either:
Override forward with the signature forward(ctx, *args, **kwargs).
setup_context
is not overridden. Setting up the ctx for backward happens inside theforward
.Override forward with the signature forward(*args, **kwargs) and override
setup_context
. Setting up the ctx for backward happens insidesetup_context
(as opposed to inside theforward
)
See
torch.autograd.Function.forward
and Extending torch.autograd for more details.
- to_save
- static vjp( ) Any
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- static vmap(
- info,
- in_dims,
- *args,
Defines a rule for the behavior of this autograd.Function underneath
torch.vmap
. For atorch.autograd.Function
to supporttorch.vmap
, you must either override this staticmethod, or setgenerate_vmap_rule
toTrue
(you may not do both).If you choose to override this staticmethod: it must accept
an
info
object as the first argument.info.batch_size
specifies the size of the dimension being vmapped over, whileinfo.randomness
is the randomness option passed totorch.vmap
.an
in_dims
tuple as the second argument. For each arg inargs
,in_dims
has a correspondingOptional[int]
. It isNone
if the arg is not a Tensor or if the arg is not being vmapped over, otherwise, it is an integer specifying what dimension of the Tensor is being vmapped over.*args
, which is the same as the args toforward
.
The return of the vmap staticmethod is a tuple of
(output, out_dims)
. Similar toin_dims
,out_dims
should be of the same structure asoutput
and contain oneout_dim
per output that specifies if the output has the vmapped dimension and what index it is in.Please see Extending torch.func with autograd.Function for more details.
- class WarpBoundPosFunction(*args, **kwargs)
Bases:
Function
- static forward(
- ctx,
- pos,
- retract_config,
- retract_idx,
- p_l,
- weight,
- activation_distance,
- null_space_weight,
- vec_weight,
- out_cost,
- out_gp,
This function is to be overridden by all subclasses. There are two ways to define forward:
Usage 1 (Combined forward and ctx):
@staticmethod def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any: pass
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See Combined or separate forward() and setup_context() for more details
Usage 2 (Separate forward and ctx):
@staticmethod def forward(*args: Any, **kwargs: Any) -> Any: pass @staticmethod def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None: pass
The forward no longer accepts a ctx argument.
Instead, you must also override the
torch.autograd.Function.setup_context
staticmethod to handle setting up thectx
object.output
is the output of the forward,inputs
are a Tuple of inputs to the forward.See Extending torch.autograd for more details
The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with
ctx.save_for_backward
if they are intended to be used inbackward
(equivalently,vjp
) orctx.save_for_forward
if they are intended to be used for injvp
.
- static backward(
- ctx,
- grad_out_cost,
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- _backward_cls
alias of
WarpBoundPosFunctionBackward
- _get_compiled_autograd_symints()
- _is_compiled_autograd_tracing()
- _materialize_non_diff_grads
- _raw_saved_tensors
- static _register_hook(
- backward_hooks,
- hook,
- _register_hook_dict()
- _sequence_nr()
- classmethod apply(
- *args,
- **kwargs,
- dirty_tensors
- generate_vmap_rule = False
- is_traceable = False
Bool that specifies if PyTorch should attempt to autogenerate
torch.vmap
support for this autograd.Function. You may set this to True only if this autograd.Function’s forward, backward, and jvp (if they exist) are written using PyTorch operations; otherwise, please overridetorch.autograd.Function.vmap
to add support fortorch.vmap
.Please see Extending torch.func with autograd.Function for more details.
- static jvp( ) Any
Defines a formula for differentiating the operation with forward mode automatic differentiation. This function is to be overridden by all subclasses. It must accept a context
ctx
as the first argument, followed by as many inputs as theforward
got (None will be passed in for non tensor inputs of the forward function), and it should return as many tensors as there were outputs toforward
. Each argument is the gradient w.r.t the given input, and each returned value should be the gradient w.r.t. the corresponding output. If an output is not a Tensor or the function is not differentiable with respect to that output, you can just pass None as a gradient for that input.You can use the
ctx
object to pass any value from the forward to this functions.
- mark_dirty(
- *args: Tensor,
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.- Examples::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Inplace(Function): >>> @staticmethod >>> def forward(ctx, x): >>> x_npy = x.numpy() # x_npy shares storage with x >>> x_npy += 1 >>> ctx.mark_dirty(x) >>> return x >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_output): >>> return grad_output >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone() >>> b = a * a >>> Inplace.apply(a) # This would lead to wrong gradients! >>> # but the engine would not know unless we mark_dirty >>> # xdoctest: +SKIP >>> b.backward() # RuntimeError: one of the variables needed for gradient >>> # computation has been modified by an inplace operation
- mark_non_differentiable(
- *args: Tensor,
Marks outputs as non-differentiable.
This should be called at most once, only from inside the
forward
method, and all arguments should be tensor 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 sort. See example::
>>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> sorted, idx = x.sort() >>> ctx.mark_non_differentiable(idx) >>> ctx.save_for_backward(x, idx) >>> return sorted, idx >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): # still need to accept g2 >>> x, idx = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> grad_input.index_add_(0, idx, g1) >>> return grad_input
- *pairs,
- materialize_grads
- maybe_clear_saved_tensors()
- metadata
- name()
- needs_input_grad
- next_functions
- non_differentiable
- register_hook()
- register_prehook()
- requires_grad
- save_for_backward(
- *tensors: Tensor,
Saves given tensors for a future call to
backward
.save_for_backward
should be called at most once, only from inside theforward
method, and only with tensors.All tensors intended to be used in the backward pass should be saved with
save_for_backward
(as opposed to directly onctx
) to prevent incorrect gradients and memory leaks, and enable the application of saved tensor hooks. Seetorch.autograd.graph.saved_tensors_hooks
.Note that if intermediary tensors, tensors that are neither inputs nor outputs of
forward
, are saved for backward, your custom Function may not support double backward. Custom Functions that do not support double backward should decorate theirbackward
method with@once_differentiable
so that performing double backward raises an error. If you’d like to support double backward, you can either recompute intermediaries based on the inputs during backward or return the intermediaries as the outputs of the custom Function. See the double backward tutorial for more details.In
backward
, saved tensors can be accessed through thesaved_tensors
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
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> w = x * z >>> out = x * y + y * z + w * y >>> ctx.save_for_backward(x, y, w, out) >>> ctx.z = z # z is not a tensor >>> return out >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_out): >>> x, y, w, out = ctx.saved_tensors >>> z = ctx.z >>> gx = grad_out * (y + y * z) >>> gy = grad_out * (x + z + w) >>> gz = None >>> return gx, gy, gz >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> d = Func.apply(a, b, c)
- save_for_forward(
- *tensors: Tensor,
Saves given tensors for a future call to
jvp
.save_for_forward
should be only called once, from inside theforward
method, and only be called with tensors.In
jvp
, saved objects can be accessed through thesaved_tensors
attribute.Arguments can also be
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +SKIP >>> class Func(torch.autograd.Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> ctx.save_for_backward(x, y) >>> ctx.save_for_forward(x, y) >>> ctx.z = z >>> return x * y * z >>> >>> @staticmethod >>> def jvp(ctx, x_t, y_t, _): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * (y * x_t + x * y_t) >>> >>> @staticmethod >>> def vjp(ctx, grad_out): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * grad_out * y, z * grad_out * x, None >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> t = torch.tensor(1., dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> >>> with fwAD.dual_level(): >>> a_dual = fwAD.make_dual(a, t) >>> d = Func.apply(a_dual, b, c)
- saved_for_forward
- saved_tensors
- saved_variables
- set_materialize_grads(
- value: bool,
Sets whether to materialize grad tensors. Default is
True
.This should be called only from inside the
forward
methodIf
True
, undefined grad tensors will be expanded to tensors full of zeros prior to calling thebackward
andjvp
methods.- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class SimpleFunc(Function): >>> @staticmethod >>> def forward(ctx, x): >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> return g1 + g2 # No check for None necessary >>> >>> # We modify SimpleFunc to handle non-materialized grad outputs >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> ctx.set_materialize_grads(False) >>> ctx.save_for_backward(x) >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> x, = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> if g1 is not None: # We must check for None now >>> grad_input += g1 >>> if g2 is not None: >>> grad_input += g2 >>> return grad_input >>> >>> a = torch.tensor(1., requires_grad=True) >>> b, _ = Func.apply(a) # induces g2 to be undefined
- static setup_context( ) Any
There are two ways to define the forward pass of an autograd.Function.
Either:
Override forward with the signature forward(ctx, *args, **kwargs).
setup_context
is not overridden. Setting up the ctx for backward happens inside theforward
.Override forward with the signature forward(*args, **kwargs) and override
setup_context
. Setting up the ctx for backward happens insidesetup_context
(as opposed to inside theforward
)
See
torch.autograd.Function.forward
and Extending torch.autograd for more details.
- to_save
- static vjp( ) Any
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- static vmap(
- info,
- in_dims,
- *args,
Defines a rule for the behavior of this autograd.Function underneath
torch.vmap
. For atorch.autograd.Function
to supporttorch.vmap
, you must either override this staticmethod, or setgenerate_vmap_rule
toTrue
(you may not do both).If you choose to override this staticmethod: it must accept
an
info
object as the first argument.info.batch_size
specifies the size of the dimension being vmapped over, whileinfo.randomness
is the randomness option passed totorch.vmap
.an
in_dims
tuple as the second argument. For each arg inargs
,in_dims
has a correspondingOptional[int]
. It isNone
if the arg is not a Tensor or if the arg is not being vmapped over, otherwise, it is an integer specifying what dimension of the Tensor is being vmapped over.*args
, which is the same as the args toforward
.
The return of the vmap staticmethod is a tuple of
(output, out_dims)
. Similar toin_dims
,out_dims
should be of the same structure asoutput
and contain oneout_dim
per output that specifies if the output has the vmapped dimension and what index it is in.Please see Extending torch.func with autograd.Function for more details.
- class WarpBoundPosLoss(*args, **kwargs)
Bases:
WarpBoundPosFunction
- static backward(
- ctx,
- grad_out_cost,
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- _backward_cls
alias of
WarpBoundPosLossBackward
- _get_compiled_autograd_symints()
- _is_compiled_autograd_tracing()
- _materialize_non_diff_grads
- _raw_saved_tensors
- static _register_hook(
- backward_hooks,
- hook,
- _register_hook_dict()
- _sequence_nr()
- classmethod apply(*args, **kwargs)
- dirty_tensors
- static forward(
- ctx,
- pos,
- retract_config,
- retract_idx,
- p_l,
- weight,
- activation_distance,
- null_space_weight,
- vec_weight,
- out_cost,
- out_gp,
This function is to be overridden by all subclasses. There are two ways to define forward:
Usage 1 (Combined forward and ctx):
@staticmethod def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any: pass
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See Combined or separate forward() and setup_context() for more details
Usage 2 (Separate forward and ctx):
@staticmethod def forward(*args: Any, **kwargs: Any) -> Any: pass @staticmethod def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None: pass
The forward no longer accepts a ctx argument.
Instead, you must also override the
torch.autograd.Function.setup_context
staticmethod to handle setting up thectx
object.output
is the output of the forward,inputs
are a Tuple of inputs to the forward.See Extending torch.autograd for more details
The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with
ctx.save_for_backward
if they are intended to be used inbackward
(equivalently,vjp
) orctx.save_for_forward
if they are intended to be used for injvp
.
- generate_vmap_rule = False
- is_traceable = False
Bool that specifies if PyTorch should attempt to autogenerate
torch.vmap
support for this autograd.Function. You may set this to True only if this autograd.Function’s forward, backward, and jvp (if they exist) are written using PyTorch operations; otherwise, please overridetorch.autograd.Function.vmap
to add support fortorch.vmap
.Please see Extending torch.func with autograd.Function for more details.
- static jvp( ) Any
Defines a formula for differentiating the operation with forward mode automatic differentiation. This function is to be overridden by all subclasses. It must accept a context
ctx
as the first argument, followed by as many inputs as theforward
got (None will be passed in for non tensor inputs of the forward function), and it should return as many tensors as there were outputs toforward
. Each argument is the gradient w.r.t the given input, and each returned value should be the gradient w.r.t. the corresponding output. If an output is not a Tensor or the function is not differentiable with respect to that output, you can just pass None as a gradient for that input.You can use the
ctx
object to pass any value from the forward to this functions.
- mark_dirty(
- *args: Tensor,
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.- Examples::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Inplace(Function): >>> @staticmethod >>> def forward(ctx, x): >>> x_npy = x.numpy() # x_npy shares storage with x >>> x_npy += 1 >>> ctx.mark_dirty(x) >>> return x >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_output): >>> return grad_output >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone() >>> b = a * a >>> Inplace.apply(a) # This would lead to wrong gradients! >>> # but the engine would not know unless we mark_dirty >>> # xdoctest: +SKIP >>> b.backward() # RuntimeError: one of the variables needed for gradient >>> # computation has been modified by an inplace operation
- mark_non_differentiable(
- *args: Tensor,
Marks outputs as non-differentiable.
This should be called at most once, only from inside the
forward
method, and all arguments should be tensor 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 sort. See example::
>>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> sorted, idx = x.sort() >>> ctx.mark_non_differentiable(idx) >>> ctx.save_for_backward(x, idx) >>> return sorted, idx >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): # still need to accept g2 >>> x, idx = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> grad_input.index_add_(0, idx, g1) >>> return grad_input
- *pairs,
- materialize_grads
- maybe_clear_saved_tensors()
- metadata
- name()
- needs_input_grad
- next_functions
- non_differentiable
- register_hook()
- register_prehook()
- requires_grad
- save_for_backward(
- *tensors: Tensor,
Saves given tensors for a future call to
backward
.save_for_backward
should be called at most once, only from inside theforward
method, and only with tensors.All tensors intended to be used in the backward pass should be saved with
save_for_backward
(as opposed to directly onctx
) to prevent incorrect gradients and memory leaks, and enable the application of saved tensor hooks. Seetorch.autograd.graph.saved_tensors_hooks
.Note that if intermediary tensors, tensors that are neither inputs nor outputs of
forward
, are saved for backward, your custom Function may not support double backward. Custom Functions that do not support double backward should decorate theirbackward
method with@once_differentiable
so that performing double backward raises an error. If you’d like to support double backward, you can either recompute intermediaries based on the inputs during backward or return the intermediaries as the outputs of the custom Function. See the double backward tutorial for more details.In
backward
, saved tensors can be accessed through thesaved_tensors
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
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> w = x * z >>> out = x * y + y * z + w * y >>> ctx.save_for_backward(x, y, w, out) >>> ctx.z = z # z is not a tensor >>> return out >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_out): >>> x, y, w, out = ctx.saved_tensors >>> z = ctx.z >>> gx = grad_out * (y + y * z) >>> gy = grad_out * (x + z + w) >>> gz = None >>> return gx, gy, gz >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> d = Func.apply(a, b, c)
- save_for_forward(
- *tensors: Tensor,
Saves given tensors for a future call to
jvp
.save_for_forward
should be only called once, from inside theforward
method, and only be called with tensors.In
jvp
, saved objects can be accessed through thesaved_tensors
attribute.Arguments can also be
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> # xdoctest: +SKIP >>> class Func(torch.autograd.Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> ctx.save_for_backward(x, y) >>> ctx.save_for_forward(x, y) >>> ctx.z = z >>> return x * y * z >>> >>> @staticmethod >>> def jvp(ctx, x_t, y_t, _): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * (y * x_t + x * y_t) >>> >>> @staticmethod >>> def vjp(ctx, grad_out): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * grad_out * y, z * grad_out * x, None >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> t = torch.tensor(1., dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> >>> with fwAD.dual_level(): >>> a_dual = fwAD.make_dual(a, t) >>> d = Func.apply(a_dual, b, c)
- saved_for_forward
- saved_tensors
- saved_variables
- set_materialize_grads(
- value: bool,
Sets whether to materialize grad tensors. Default is
True
.This should be called only from inside the
forward
methodIf
True
, undefined grad tensors will be expanded to tensors full of zeros prior to calling thebackward
andjvp
methods.- Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) >>> class SimpleFunc(Function): >>> @staticmethod >>> def forward(ctx, x): >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> return g1 + g2 # No check for None necessary >>> >>> # We modify SimpleFunc to handle non-materialized grad outputs >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> ctx.set_materialize_grads(False) >>> ctx.save_for_backward(x) >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> x, = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> if g1 is not None: # We must check for None now >>> grad_input += g1 >>> if g2 is not None: >>> grad_input += g2 >>> return grad_input >>> >>> a = torch.tensor(1., requires_grad=True) >>> b, _ = Func.apply(a) # induces g2 to be undefined
- static setup_context( ) Any
There are two ways to define the forward pass of an autograd.Function.
Either:
Override forward with the signature forward(ctx, *args, **kwargs).
setup_context
is not overridden. Setting up the ctx for backward happens inside theforward
.Override forward with the signature forward(*args, **kwargs) and override
setup_context
. Setting up the ctx for backward happens insidesetup_context
(as opposed to inside theforward
)
See
torch.autograd.Function.forward
and Extending torch.autograd for more details.
- to_save
- static vjp( ) Any
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward
will havectx.needs_input_grad[0] = True
if the first input toforward
needs gradient computed w.r.t. the output.
- static vmap(
- info,
- in_dims,
- *args,
Defines a rule for the behavior of this autograd.Function underneath
torch.vmap
. For atorch.autograd.Function
to supporttorch.vmap
, you must either override this staticmethod, or setgenerate_vmap_rule
toTrue
(you may not do both).If you choose to override this staticmethod: it must accept
an
info
object as the first argument.info.batch_size
specifies the size of the dimension being vmapped over, whileinfo.randomness
is the randomness option passed totorch.vmap
.an
in_dims
tuple as the second argument. For each arg inargs
,in_dims
has a correspondingOptional[int]
. It isNone
if the arg is not a Tensor or if the arg is not being vmapped over, otherwise, it is an integer specifying what dimension of the Tensor is being vmapped over.*args
, which is the same as the args toforward
.
The return of the vmap staticmethod is a tuple of
(output, out_dims)
. Similar toin_dims
,out_dims
should be of the same structure asoutput
and contain oneout_dim
per output that specifies if the output has the vmapped dimension and what index it is in.Please see Extending torch.func with autograd.Function for more details.