nn.modules.ExpSynExodus
- class nn.modules.ExpSynExodus(*args, **kwargs)[source]
Bases:
LIFBaseTorch
Attributes overview
Class name of
self
The full name of this module (class plus module name)
The name of this module, or an empty string if
None
The shape of this module
(DEPRECATED) The output size of this module
The input size of this module
The output size of this module
If
True
, this module receives spiking input.If
True
, this module sends spiking output.(str) The mode by which leaks are determined for this module.
(int) Number of input synapses per neuron
(float) Euler simulator time-step in seconds
(Tensor) Recurrent weights
(Nout, Nin)
(float) Noise std.dev.
(Tensor) Membrane time constants
(Nout,)
or()
(Tensor) Synaptic time constants
(Nin,)
or()
(Tensor) Membrane decay factor
(Nout,)
or()
(Tensor) Synaptic decay factor
(Nin,)
or()
(Tensor) membrane bitshift in xylo
(Nout,)
or()
(Tensor) synaptic bitshift in xylo
(Nout,)
or()
(Tensor) Neuron biases
(Nout,)
or()
(Tensor) Firing threshold for each neuron
(Nout,)
(float) Learning window cutoff for surrogate gradient function
(Tensor) Membrane potentials
(Nout,)
(Tensor) Synaptic currents
(Nin,)
(Tensor) Spikes
(Nin,)
(Callable) Spike generation function with surrograte gradient
(float) Maximum number of events that can be produced in each time-step
Methods overview
__init__
(shape[, tau, noise_std, dt])Instantiate an exponential synapse module using the Exodus backend
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.as_graph
()Convert this module to a computational graph
attributes_named
(name)Search for attributes of this or submodules by time
bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module's forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
evolve
(input_data[, record])Implement the Rockpool low-level evolution API
Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.forward
(data)forward method for processing data through this layer Adds inputs to the synaptic states
from_torch
(obj[, retain_torch_api])Convert a torch module into a Rockpool
TorchModule
in-placeget_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.Return any extra state to include in the module's state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
json_to_param
(jparam)load
(fn)load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.merge
(a, b)modules
(*args, **kwargs)Return a dictionary of all sub-modules of this module
named_buffers
([prefix, recurse, ...])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, ...])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
param_to_json
(param)parameters
([family])Return a nested dictionary of module and submodule Parameters
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, ...])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, ...])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
Register a post hook to be run after module's
load_state_dict
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
Reset all parameters in this module
Reset the state of this module
save
(fn)set_attributes
(new_attributes)Set the attributes and sub-module attributes from a dictionary
set_extra_state
(state)Set extra state contained in the loaded
state_dict
.See
torch.Tensor.share_memory_()
.simulation_parameters
([family])Return a nested dictionary of module and submodule SimulationParameters
state
([family])Return a nested dictionary of module and submodule States
state_dict
(*args[, destination, prefix, ...])Return a dictionary containing references to the whole state of the module.
timed
([output_num, dt, add_events])Convert this module to a
TimedModule
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
to_json
()to_torch
([use_torch_call])Convert the module to use the torch.nn.Module API
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
- T_destination = ~T_destination
- __init__(shape: tuple, tau: float | ParameterBase = 0.05, noise_std: float | ParameterBase = 0.0, dt: float = 0.001, *args, **kwargs)[source]
Instantiate an exponential synapse module using the Exodus backend
Uses the Exodus accelerated CUDA module to implement an exponential synapse. A CUDA device is required to instantiate this module.
The output of evolving this module is the synaptic currents.
Warning
Exodus does not support noise injection.
Examples
Instantitate an exponential synapse module with 2 synapses.
>>> mod = LIFExodus(2)
Specify the synaptic time constants, as well as time-step
dt
.>>> mod = LIFExodus(2, tau_syn = 10e-3, dt = 10e-3)
Specify multiple synaptic time constants.
>>> mod = LIFExodus(2, tau_syn = [10e-3, 20e-3])
Pass the model and data to the same cuda device, since it is required to use CUDA on this module.
>>> data = torch.ones((1, 10, 4)) >>> device = 'cuda: 1' >>> mod.to(device) >>> data = data.to(device) >>> output = mod(data)
- Parameters:
shape (tuple) – The shape of this module
tau_syn (float) – An optional array with concrete initialisation data for the synaptic time constants. If not provided, 50ms will be used by default.
dt (float) – Time step in seconds. Default: 1 ms.
- _abc_impl = <_abc._abc_data object>
- _apply(fn, recurse=True)
- _auto_batch(data: Tensor, states: Tuple = (), target_shapes: Tuple = None) Tuple[Tensor, Tuple[Tensor]]
Automatically replicate states over batches and verify input dimensions
- Usage:
>>> data, (state0, state1, state2) = self._auto_batch(data, (self.state0, self.state1, self.state2))
This will verify that
data
has the correct final dimension (i.e.self.size_in
). Ifdata
has only two dimensions(T, Nin)
, then it will be augmented to(1, T, Nin)
. The individual states will be replicated out from shape(a, b, c, ...)
to(n_batches, a, b, c, ...)
and returned.
- Parameters:
data (torch.Tensor) – Input data tensor. Either
(batches, T, Nin)
or(T, Nin)
states (Tuple) – Tuple of state variables. Each will be replicated out over batches by prepending a batch dimension
- Returns:
(torch.Tensor, Tuple[torch.Tensor]) data, states
- _backward_hooks: Dict[int, Callable]
- _backward_pre_hooks: Dict[int, Callable]
- _buffers: Dict[str, Tensor | None]
- _call_impl(*args, **kwargs)
- _compiled_call_impl: Callable | None = None
- _force_set_attributes
(bool) If
True
, do not sanity-check attributes when setting.
- _forward_hooks: Dict[int, Callable]
- _forward_hooks_always_called: Dict[int, bool]
- _forward_hooks_with_kwargs: Dict[int, bool]
- _forward_pre_hooks: Dict[int, Callable]
- _forward_pre_hooks_with_kwargs: Dict[int, bool]
- _get_all_leak_params()
Calculate and return all decay parameters, depending on leak mode
- _get_attribute_family(type_name: str, family: str | Tuple | List = None) dict
Search for attributes of this module and submodules that match a given family
This method can be used to conveniently get all weights for a network; or all time constants; or any other family of parameters. Parameter families are defined simply by a string:
"weights"
for weights;"taus"
for time constants, etc. These strings are arbitrary, but if you follow the conventions then future developers will thank you (that includes you in six month’s time).- Parameters:
type_name (str) – The class of parameters to search for. Must be one of
["Parameter", "SimulationParameter", "State"]
or another future subclass ofParameterBase
family (Union[str, Tuple[str]]) – A string or list or tuple of strings, that define one or more attribute families to search for
- Returns:
A nested dictionary of attributes that match the provided
type_name
andfamily
- Return type:
dict
- _get_attribute_registry() Tuple[Dict, Dict]
Return or initialise the attribute registry for this module
- Returns:
registered_attributes, registered_modules
- Return type:
(tuple)
- _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()
- _has_registered_attribute(name: str) bool
Check if the module has a registered attribute
- Parameters:
name (str) – The name of the attribute to check
- Returns:
True
if the attributename
is in the attribute registry,False
otherwise.- Return type:
bool
- _in_Module_init
(bool) If exists and
True
, indicates that the module is in the__init__
chain.
- _is_full_backward_hook: bool | None
- _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 atlocal_metadata.get("version", None)
. Additionally,local_metadata
can also contain the keyassign_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()
- _load_state_dict_post_hooks: Dict[int, Callable]
- _load_state_dict_pre_hooks: Dict[int, Callable]
- _maybe_warn_non_full_backward_hook(inputs, result, grad_fn)
- _modules: Dict[str, 'Module' | None]
- _name: str | None
Name of this module, if assigned
- _named_members(get_members_fn, prefix='', recurse=True, remove_duplicate: bool = True)
Help yield various names + members of modules.
- _non_persistent_buffers_set: Set[str]
- _register_attribute(name: str, val: ParameterBase)
Record an attribute in the attribute registry
- Parameters:
name (str) – The name of the attribute to register
val (ParameterBase) – The
ParameterBase
subclass object to register. e.g.Parameter
,SimulationParameter
orState
.
- _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 loadingstate_dict
intoself
. 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_module(name: str, mod)
Add a submodule to the module registry
- Parameters:
name (str) – The name of the submodule, extracted from the assigned attribute name
mod (TorchModule) – The submodule to register
- Raises:
ValueError – If the assigned submodule is not a
TorchModule
- _register_state_dict_hook(hook)
Register a state-dict hook.
These hooks will be called with arguments:
self
,state_dict
,prefix
,local_metadata
, after thestate_dict
ofself
is set. Note that only parameters and buffers ofself
or its children are guaranteed to exist instate_dict
. The hooks may modifystate_dict
inplace or return a new one.
- _replicate_for_data_parallel()
- _reset_attribute(name: str) ModuleBase
Reset an attribute to its initialisation value
- Parameters:
name (str) – The name of the attribute to reset
- Returns:
For compatibility with the functional API
- Return type:
self (
Module
)
- _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 instate_dict()
.In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic.
- Parameters:
destination (dict) – a dict where state will be stored
prefix (str) – the prefix for parameters and buffers used in this module
- _set_leak_param(name, value)
Set the value of a named decay parameter, depending on leak mode
- _shape
The shape of this module
- _slow_forward(*input, **kwargs)
- _spiking_input: bool
Whether this module receives spiking input
- _spiking_output: bool
Whether this module produces spiking output
- _state_dict_hooks: Dict[int, Callable]
- _state_dict_pre_hooks: Dict[int, Callable]
- _submodulenames: List[str]
Registry of sub-module names
- _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.
- _wrap_recorded_state(recorded_dict: dict, t_start: float) Dict[str, TimeSeries]
Convert a recorded dictionary to a
TimeSeries
representationThis method is optional, and is provided to make the
timed()
conversion to aTimedModule
work better. You should override this method in your customModule
, to wrap each element of your recorded state dictionary as aTimeSeries
- Parameters:
state_dict (dict) – A recorded state dictionary as returned by
evolve()
t_start (float) – The initial time of the recorded state, to use as the starting point of the time series
- Returns:
The mapped recorded state dictionary, wrapped as
TimeSeries
objects- Return type:
Dict[str, TimeSeries]
- _wrapped_call_impl(*args, **kwargs)
- add_module(name: str, module: Module | None) 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.
- alpha: P_tensor
(Tensor) Membrane decay factor
(Nout,)
or()
- apply(fn: Callable[[Module], None]) 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 nn-init-doc).
- Parameters:
fn (
Module
-> None) – function to be applied to each submodule- Returns:
self
- Return type:
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) )
- as_graph() GraphModuleBase
Convert this module to a computational graph
- Returns:
The computational graph corresponding to this module
- Return type:
- Raises:
NotImplementedError – If
as_graph()
is not implemented for this subclass
- attributes_named(name: Tuple[str] | List[str] | str) dict
Search for attributes of this or submodules by time
- Parameters:
name (Union[str, Tuple[str]) – The name of the attribute to search for
- Returns:
A nested dictionary of attributes that match
name
- Return type:
dict
- beta: P_tensor
(Tensor) Synaptic decay factor
(Nin,)
or()
- bfloat16() T
Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns:
self
- Return type:
- bias: P_tensor
(Tensor) Neuron biases
(Nout,)
or()
- buffers(recurse: bool = True) Iterator[Tensor]
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)
- call_super_init: bool = False
- children() Iterator[Module]
Return an iterator over immediate children modules.
- Yields:
Module – a child module
- property class_name: str
Class name of
self
- Type:
str
- 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 totorch.compile()
.See
torch.compile()
for details on the arguments for this function.
- cpu() T
Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns:
self
- Return type:
- cuda(device: int | device | None = None) 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:
- dash_mem: P_tensor
(Tensor) membrane bitshift in xylo
(Nout,)
or()
- dash_syn: P_tensor
(Tensor) synaptic bitshift in xylo
(Nout,)
or()
- double() T
Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns:
self
- Return type:
- dt: P_float
(float) Euler simulator time-step in seconds
- dump_patches: bool = False
- 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-disable-grad-doc for a comparison between
eval()
and several similar mechanisms that may be confused with it.- Returns:
self
- Return type:
- evolve(input_data: Tensor, record: bool = False) Tuple[Any, Any, Any]
Implement the Rockpool low-level evolution API
evolve()
is provided byTorchModule
to connect the Rockpool low-level API to the Torch API (i.e.forward()
etc.). You should not overrideevolve()
if usingTorchModule
directly, but should implement the Torch API to perform evaluation of the module.evolve()
will automatically set the_record
flag according to the input argument toevolve()
. You can use this within yourforward()
method, and should build a dictionary_record_dict
. This will be returned automatically fromevolve()
, if requested.- Parameters:
input_data – This might be a numpy array or Torch tensor, containing the input data to evolve over
record (bool) – Iff
True
, return a dictionary of state variables asrecord_dict
, containing the time series of those state variables over evolution. Default:False
, do not record state during evolution
- Returns:
- (output_data, new_states, record_dict)
output_data
is the output from theTorchModule
, probably as a torchTensor
.new_states
is a dictionary containing the updated state for this module, post evolution. If therecord
argument isTrue
,record_dict
is a dictionary containing the recorded state variables for this and all submodules, recorded over evolution.
- Return type:
(array, dict, dict)
- 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:
- forward(data: Tensor) Tensor [source]
forward method for processing data through this layer Adds inputs to the synaptic states
- Parameters:
data (torch.Tensor) – Data takes the shape of (batch, time_steps, n_synapses)
- Returns:
Out of spikes with the shape (batch, time_steps, n_synapses)
- Return type:
torch.Tensor
- classmethod from_torch(obj: Module, retain_torch_api: bool = False) None
Convert a torch module into a Rockpool
TorchModule
in-place- Parameters:
obj (torch.nn.Module) – Torch module to convert to a Rockpool
retain_torch_api (bool) – If
True
, calling the resulting module will use the Torch API. Default:False
, convert the module to the Rockpool low-level API for__call__()
.
- property full_name: str
The full name of this module (class plus module name)
- Type:
str
- get_buffer(target: str) Tensor
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:
torch.Tensor
- 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’sstate_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:
object
- get_parameter(target: str) Parameter
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) Module
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:
torch.nn.Module
- 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:
- ipu(device: int | device | None = None) 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:
- isyn: P_tensor
(Tensor) Synaptic currents
(Nin,)
- json_to_param(jparam)
- leak_mode
(str) The mode by which leaks are determined for this module.
- learning_window: P_tensor
(float) Learning window cutoff for surrogate gradient function
- load(fn)
- load_state_dict(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)
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
unlessget_swap_module_params_on_conversion()
isTrue
.- 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) – 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. The only exception is therequires_grad
field ofDefault: ``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
.
- max_spikes_per_dt: P_float
(float) Maximum number of events that can be produced in each time-step
- merge(a, b)
- modules(*args, **kwargs)
Return a dictionary of all sub-modules of this module
- Returns:
A dictionary containing all sub-modules. Each item will be named with the sub-module name.
- Return type:
dict
- n_synapses: P_int
(int) Number of input synapses per neuron
- property name: str
The name of this module, or an empty string if
None
- Type:
str
- named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) 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(memo: Set[Module] | None = None, prefix: str = '', remove_duplicate: bool = True)
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(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) 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())
- noise_std: P_float
(float) Noise std.dev. injected onto the membrane of each neuron during evolution
- param_to_json(param)
- parameters(family: str | Tuple | List = None) Dict
Return a nested dictionary of module and submodule Parameters
Use this method to inspect the Parameters from this and all submodules. The optional argument
family
allows you to search for Parameters in a particular family — for example"weights"
for all weights of this module and nested submodules.Although the
family
argument is an arbitrary string, reasonable choises are"weights"
,"taus"
for time constants,"biases"
for biases…Examples
Obtain a dictionary of all Parameters for this module (including submodules):
>>> mod.parameters() dict{ ... }
Obtain a dictionary of Parameters from a particular family:
>>> mod.parameters("weights") dict{ ... }
- Parameters:
family (str) – The family of Parameters to search for. Default:
None
; return all parameters.- Returns:
A nested dictionary of Parameters of this module and all submodules
- Return type:
dict
- register_backward_hook(hook: Callable[[Module, Tuple[Tensor, ...] | Tensor, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor]) RemovableHandle
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(name: str, tensor: Tensor, persistent: bool = True, *args, **kwargs) 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) RemovableHandle
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) RemovableHandle
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) RemovableHandle
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) RemovableHandle
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_module(name: str, module: Module | None) None
Alias for
add_module()
.
- register_parameter(name: str, param: 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
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) T
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-disable-grad-doc 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:
- reset_parameters()
Reset all parameters in this module
- Returns:
The updated module is returned for compatibility with the functional API
- Return type:
- reset_state() ModuleBase
Reset the state of this module
- Returns:
The updated module is returned for compatibility with the functional API
- Return type:
- save(fn)
- set_attributes(new_attributes: dict) ModuleBase
Set the attributes and sub-module attributes from a dictionary
This method can be used with the dictionary returned from module evolution to set the new state of the module. It can also be used to set multiple parameters of a module and submodules.
Examples
Use the functional API to evolve, obtain new states, and set those states:
>>> _, new_state, _ = mod(input) >>> mod = mod.set_attributes(new_state)
Obtain a parameter dictionary, modify it, then set the parameters back:
>>> params = mod.parameters() >>> params['w_input'] *= 0. >>> mod.set_attributes(params)
- Parameters:
new_attributes (dict) – A nested dictionary containing parameters of this module and sub-modules.
- set_extra_state(state: Any) None
Set extra state contained in the loaded
state_dict
.This function is called from
load_state_dict()
to handle any extra state found within thestate_dict
. Implement this function and a correspondingget_extra_state()
for your module if you need to store extra state within itsstate_dict
.- Parameters:
state (dict) – Extra state from the
state_dict
- property shape: tuple
The shape of this module
- Type:
tuple
See
torch.Tensor.share_memory_()
.
- simulation_parameters(family: str | Tuple | List = None) Dict
Return a nested dictionary of module and submodule SimulationParameters
Use this method to inspect the SimulationParameters from this and all submodules. The optional argument
family
allows you to search for SimulationParameters in a particular family.Examples
Obtain a dictionary of all SimulationParameters for this module (including submodules):
>>> mod.simulation_parameters() dict{ ... }
- Parameters:
family (str) – The family of SimulationParameters to search for. Default:
None
; return all SimulationParameter attributes.- Returns:
A nested dictionary of SimulationParameters of this module and all submodules
- Return type:
dict
- property size: int
(DEPRECATED) The output size of this module
- Type:
int
- property size_in: int
The input size of this module
- Type:
int
- property size_out: int
The output size of this module
- Type:
int
- spike_generation_fn: P_Callable
(Callable) Spike generation function with surrograte gradient
- spikes: P_tensor
(Tensor) Spikes
(Nin,)
- property spiking_input: bool
If
True
, this module receives spiking input. IfFalse
, this module expects continuous input.- Type:
bool
- property spiking_output
If
True
, this module sends spiking output. IfFalse
, this module sends continuous output.- Type:
bool
- state(family: str | Tuple | List = None) Dict
Return a nested dictionary of module and submodule States
Use this method to inspect the States from this and all submodules. The optional argument
family
allows you to search for States in a particular family.Examples
Obtain a dictionary of all States for this module (including submodules):
>>> mod.state() dict{ ... }
- Parameters:
family (str) – The family of States to search for. Default:
None
; return all State attributes.- Returns:
A nested dictionary of States of this module and all submodules
- Return type:
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:
dict
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
- tau_mem: P_tensor
(Tensor) Membrane time constants
(Nout,)
or()
- tau_syn: P_tensor
(Tensor) Synaptic time constants
(Nin,)
or()
- threshold: P_tensor
(Tensor) Firing threshold for each neuron
(Nout,)
- timed(output_num: int = 0, dt: float = None, add_events: bool = False)
Convert this module to a
TimedModule
- Parameters:
output_num (int) – Specify which output of the module to take, if the module returns multiple output series. Default:
0
, take the first (or only) output.dt (float) – Used to provide a time-step for this module, if the module does not already have one. If
self
already defines a time-step, thenself.dt
will be used. Default:None
add_events (bool) – Iff
True
, theTimedModule
will add events occurring on a single timestep on input and output. Default:False
, don’t add time steps.
Returns:
TimedModule
: A timed module that wraps this module
- 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:
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(*, device: int | str | device | None, recurse: bool = True) 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:
- to_json()
- to_torch(use_torch_call: bool = True)
Convert the module to use the torch.nn.Module API
This method exposes the torch API for
.__call__()
,.parameters()
and.__repr__()
methods, recursively. By default,.__call__()
is only replaced on the top-level module. This is to ensure that the nested.forward()
methods do not break.- Parameters:
use_torch_call (bool) – Use the torch-type
__call__()
method for this object- Returns:
The converted object
- train(mode: bool = True) T
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:
- training: bool
- type(dst_type: dtype | str) 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:
- vmem: P_tensor
(Tensor) Membrane potentials
(Nout,)
- w_rec: P_ndarray
(Tensor) Recurrent weights
(Nout, Nin)
- xpu(device: int | device | None = None) 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:
- zero_grad(set_to_none: bool = True) None
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.