devices.xylo.syns61201.XyloSimο
- class devices.xylo.syns61201.XyloSim(*args, **kwargs)[source]ο
Bases:
XyloSim
A
Module
simulating a digital SNN on Xylo, using XyloSim as a back-end.You should use the factory methods
from_config
andfrom_specification
to build a concreteXyloSim
module.See also
See the tutorials π Overview of the Xylo family and Training a spiking network to deploy to the Xylo digital SNN for a high-level overview of building and deploying networks for Xylo.
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
Private key to ensure factory creation
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.(XyloConfiguration) Configuration of the Xylo module
(float) Simulation time-step for this module
Methods overview
__init__
(create_key,Β config[,Β shape,Β dt,Β ...])Private constructor for
XyloSim
as_graph
()Convert this module to a computational graph
attributes_named
(name)Search for attributes of this or submodules by time
evolve
([input_raster,Β record])Evolve the state of this module over input data
from_config
(config[,Β dt,Β output_mode])Create a XyloSim based layer to simulate the Xylo hardware, from a configuration
from_specification
(weights_in,Β weights_out)Instantiate a
XyloSim
module from a full set of parametersmodules
()Return a dictionary of all sub-modules of this module
parameters
([family])Return a nested dictionary of module and submodule Parameters
Reset all parameters in this module
Reset the state of this module.
set_attributes
(new_attributes)Set the attributes and sub-module attributes from a dictionary
simulation_parameters
([family])Return a nested dictionary of module and submodule SimulationParameters
state
([family])Return a nested dictionary of module and submodule States
timed
([output_num,Β dt,Β add_events])Convert this module to a
TimedModule
- __init__(create_key, config: Dict | Any, shape: tuple = (16, 1000, 8), dt: float = 0.001, output_mode: str = 'Spike', *args, **kwargs)[source]ο
Private constructor for
XyloSim
Warning
Use the factory methods
XyloSim.from_config()
andXyloSim.from_specfication()
to construct aXyloSim
module.
- _abc_impl = <_abc._abc_data object>ο
- _auto_batch(data: ndarray, states: Tuple = (), target_shapes: Tuple = None) Tuple[ndarray, Tuple[ndarray]] ο
Automatically replicate states over batches and verify input dimensions
Examples
>>> 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
).If
data
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.If
data
has only a single dimension(T,)
, it will be expanded to(1, T, self.size_in)
.state0
,state1
,state2
will be replicated out along the batch dimension.>>> data, (state0,) = self._auto_batch(data, (self.state0,), ((10, -1, self.size_in),))
Attempt to replicate
state0
to a specified size(10, -1, self.size_in)
.- Parameters:
data (np.ndarray) β Input data tensor. Either
(batches, T, Nin)
or(T, Nin)
states (Tuple) β Tuple of state variables. Each will be replicated out over batches by prepending a batch dimension
target_shapes (Tuple) β A tuple of target size tuples, each corresponding to each state argument. The individual states will be replicated out to match the corresponding target sizes. If not provided (the default), then states will be only replicated along batches.
- Returns:
(np.ndarray, Tuple[np.ndarray]) data, states
- _force_set_attributesο
(bool) If
True
, do not sanity-check attributes when setting.
- _get_attribute_family(type_name: str, family: str | Tuple | List = None) dict ο
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 monthβs time).- Parameters:
type_name (str) β The class of parameters to search for. Must be one of
["Parameter", "SimulationParameter", "State"]
or another future subclass ofParameterBase
family (Union[str, Tuple[str]]) β 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] ο
Return or initialise the attribute registry for this module
- Returns:
registered_attributes, registered_modules
- Return type:
(tuple)
- _has_registered_attribute(name: str) bool ο
Check if the module has a registered attribute
- Parameters:
name (str) β The name of the attribute to check
- Returns:
True
if the attributename
is in the attribute registry,False
otherwise.- Return type:
bool
- _in_Module_initο
(bool) If exists and
True
, indicates that the module is in the__init__
chain.
- _name: str | Noneο
Name of this module, if assigned
- _register_attribute(name: str, val: ParameterBase)ο
Record an attribute in the attribute registry
- Parameters:
name (str) β The name of the attribute to register
val (ParameterBase) β The
ParameterBase
subclass object to register. e.g.Parameter
,SimulationParameter
orState
.
- _register_module(name: str, mod: ModuleBase)ο
Register a sub-module in the module registry
- Parameters:
name (str) β The name of the module to register
mod (ModuleBase) β The
ModuleBase
object to register
- _reset_attribute(name: str) ModuleBase ο
Reset an attribute to its initialisation value
- Parameters:
name (str) β The name of the attribute to reset
- Returns:
For compatibility with the functional API
- Return type:
self (
Module
)
- _shapeο
The shape of this module
- _spiking_input: boolο
Whether this module receives spiking input
- _spiking_output: boolο
Whether this module produces spiking output
- _submodulenames: List[str]ο
Registry of sub-module names
- _wrap_recorded_state(state_dict: dict, t_start: float = 0.0) dict [source]ο
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) β A recorded state dictionary as returned by
evolve()
t_start (float) β 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]
- _xylo_layer: XyloLayer | Noneο
(XyloLayer) Handle to a XyloSim object
- as_graph() GraphModuleBase ο
Convert this module to a computational graph
- Returns:
The computational graph corresponding to this module
- Return type:
- Raises:
NotImplementedError β If
as_graph()
is not implemented for this subclass
- attributes_named(name: Tuple[str] | List[str] | str) dict ο
Search for attributes of this or submodules by time
- Parameters:
name (Union[str, Tuple[str]) β The name of the attribute to search for
- Returns:
A nested dictionary of attributes that match
name
- Return type:
dict
- property class_name: strο
Class name of
self
- Type:
str
- dt: float | SimulationParameterο
(float) Simulation time-step for this module
- evolve(input_raster: ndarray = None, record: bool = False, *args, **kwargs)[source]ο
Evolve the state of this module over input data
NOTE: THIS MODULE CLASS DOES NOT PROVIDE DOCUMENTATION FOR ITS EVOLVE METHOD. PLEASE UPDATE THE DOCUMENTATION FOR THIS MODULE.
- Parameters:
input_data β The input data with shape
(T, size_in)
to evolve withrecord (bool) β If
True
, the module should record internal state during evolution and return the record. IfFalse
, no recording is required. Default:False
.
- Returns:
- (output, new_state, record)
output (np.ndarray): The output response of this module with shape
(T, size_out)
new_state (dict): A dictionary containing the updated state of this and all submodules after evolution record (dict): A dictionary containing recorded state of this and all submodules, if requested using therecord
argument
- Return type:
tuple
- classmethod from_config(config: Dict | Any, dt: float = 0.001, output_mode: str = 'Spike') XyloSim [source]ο
Create a XyloSim based layer to simulate the Xylo hardware, from a configuration
- Parameters:
config (XyloConfiguration) β
samna.xylo.XyloConfiguration
object to specify all parameters. See samna documentation for details.dt (float, optional) β Timestep for simulation. Defaults to 1e-3.
output_mode (str, optional) β readout mode. one of [βIsynβ, βVmemβ, βSpikeβ]. Defaults to βSpikeβ.
- Returns:
XyloSim object instance
- Return type:
- classmethod from_specification(weights_in: ndarray, weights_out: ndarray, weights_rec: ndarray | None = None, dash_mem: ndarray | None = None, dash_mem_out: ndarray | None = None, dash_syn: ndarray | None = None, dash_syn_2: ndarray | None = None, dash_syn_out: ndarray | None = None, threshold: ndarray | None = None, threshold_out: ndarray | None = None, bias: ndarray | None = None, bias_out: ndarray | None = None, weight_shift_in: int = 0, weight_shift_rec: int = 0, weight_shift_out: int = 0, aliases: list | None = None, dt: float = 0.001, verify_config: bool = True, output_mode: str = 'Spike') XyloSim [source]ο
Instantiate a
XyloSim
module from a full set of parameters- Parameters:
weights_in (np.ndarray) β An int8 matrix
(Nin, Nhidden, 2)
, specifying input to hidden neuron connections. The final dimension specifies the inputs to the two available synapses of the hidden neurons.weights_out (np.ndarray) β An int8 matrix
(Nhidden, Nout)
, specifying hidden to output connections.weights_rec (Optional[np.ndarray]) β An int8 matrix
(Nhidden, Nhidden, 2)
, specifying recurrent connections within the hidden population. The final dimension specifies the input to the two available synapses on each hidden neuron. Default:0
, no recurrent connections.dash_mem (Optional[np.ndarray]) β An int8 matrix
(Nhidden)
, specifying the bitshift decay value for each hidden neuron membrane potential. Default:1
.dash_mem_out (Optional[np.ndarray]) β An int8 matrix
(Nout)
, specifying the bitshift decay value for each output neuron membrane potential. Default:1
.dash_syn (Optional[np.ndarray]) β An int8 matrix
(Nhidden)
, specifying the bitshift decay value for each hidden neuron synaptic current number 1. Default:1
.dash_syn_2 (Optional[np.ndarray]) β An int8 matrix
(Nhidden)
, specifying the bitshift decay value for each hidden neuron synaptic current number 2. Default:1
.dash_syn_out (Optional[np.ndarray]) β An int8 matrix
(Nout)
, specifying the bitshift decay value for each output neuron synaptic current. Default:1
.threshold (Optional[np.ndarray]) β An int8 matrix
(Nhidden)
, specifying the firing threshold for each hidden neuron. Default:0
.threshold_out (Optional[np.ndarray]) β An int8 matrix
(Nhidden)
, specifying the firing threshold for each output neuron. Default:0
.bias (Optional[np.ndarray]) β An int8 matrix
(Nhidden)
, specifying the bias for each hidden neuron. Default:0
.bias_out (Optional[np.ndarray]) β An int8 matrix
(Nhidden)
, specifying the bias for each output neuron. Default:0
.weight_shift_in (int) β An integer number of bits to left-shift the input weight matrix
weight_shift_rec (int) β An integer number of bits to left-shift the hidden weight matrix
weight_shift_out (int) β An integer number of bits to left-shift the output weight matrix
aliases (Optional[list])
dt (float) β Simulation time step in seconds. Default: 1 ms
verify_config (bool) β Check for a valid configuraiton before applying it. Default
True
.
- Returns:
A
Module
that emulates the Xylo hardware.- Return type:
- Raises:
ValueError β If
verify_config
isTrue
and the configuration is not valid.
- property full_name: strο
The full name of this module (class plus module name)
- Type:
str
- modules() Dict ο
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
- property name: strο
The name of this module, or an empty string if
None
- Type:
str
- output_mode = 'Spike'ο
Private key to ensure factory creation
- parameters(family: str | Tuple | List = None) Dict ο
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 β 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 biasesβ¦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) β 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
- reset_parameters()ο
Reset all parameters in this module
- Returns:
The updated module is returned for compatibility with the functional API
- Return type:
- set_attributes(new_attributes: dict) ModuleBase ο
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) β A nested dictionary containing parameters of this module and sub-modules.
- property shape: tupleο
The shape of this module
- Type:
tuple
- simulation_parameters(family: str | Tuple | List = None) Dict ο
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) β 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: intο
(DEPRECATED) The output size of this module
- Type:
int
- property size_in: intο
The input size of this module
- Type:
int
- property size_out: intο
The output size of this module
- Type:
int
- property spiking_input: boolο
If
True
, this module receives spiking input. IfFalse
, this module expects continuous input.- Type:
bool
- property spiking_outputο
If
True
, this module sends spiking output. IfFalse
, this module sends continuous output.- Type:
bool
- state(family: str | Tuple | List = None) Dict ο
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) β 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
- timed(output_num: int = 0, dt: float = None, add_events: bool = False)ο
Convert this module to a
TimedModule
- Parameters:
output_num (int) β 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) β 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) β Iff
True
, theTimedModule
will add events occurring on a single timestep on input and output. Default:False
, donβt add time steps.
Returns:
TimedModule
: A timed module that wraps this module