Source code for sinabs.backend.dynapcnn.config_builder
import time
from abc import ABC, abstractmethod
from typing import List
import samna
from .dvs_layer import DVSLayer
from .mapping import LayerConstraints, get_valid_mapping
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class ConfigBuilder(ABC):
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@classmethod
@abstractmethod
def get_samna_module(self):
"""Get the saman parent module that hosts all the appropriate sub-modules and classes.
Returns
-------
samna module
"""
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@classmethod
@abstractmethod
def get_default_config(cls):
"""
Returns
-------
Returns the default configuration for the device type
"""
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@classmethod
@abstractmethod
def build_config(cls, model: "DynapcnnNetwork", chip_layers: List[int]):
"""Build the configuration given a model.
Parameters
----------
model:
The target model
chip_layers:
Chip layers where the given model layers are to be mapped.
Returns
-------
Samna Configuration object
"""
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@classmethod
@abstractmethod
def get_constraints(cls) -> List[LayerConstraints]:
"""Returns the layer constraints of a the given device.
Returns
-------
List[LayerConstraints]
"""
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@classmethod
@abstractmethod
def monitor_layers(cls, config, layers: List[int]):
"""Enable the monitor for a given set of layers in the config object."""
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@classmethod
def get_valid_mapping(cls, model: "DynapcnnNetwork") -> List[int]:
"""Find a valid set of layers for a given model.
Parameters
----------
model (DynapcnnNetwork):
A model
Returns
-------
List of core indices corresponding to each layer of the model:
The index of the core on chip to which the i-th layer in the
model is mapped is the value of the i-th entry in the list.
"""
mapping = get_valid_mapping(model, cls.get_constraints())
# turn the mapping into a dict
mapping = {m[0]: m[1] for m in mapping}
# Check if there is a dvs layer in the model
num_dynapcnn_cores = len(model.sequence)
if isinstance(model.sequence[0], DVSLayer):
num_dynapcnn_cores -= 1
# apply the mapping
chip_layers_ordering = [mapping[i] for i in range(num_dynapcnn_cores)]
return chip_layers_ordering
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@classmethod
def validate_configuration(cls, config) -> bool:
"""Check if a given configuration is valid.
Parameters
----------
config:
Configuration object
Returns
-------
True if the configuration is valid, else false
"""
is_valid, message = cls.get_samna_module().validate_configuration(config)
if not is_valid:
print(message)
return is_valid
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@classmethod
@abstractmethod
def get_output_buffer(cls):
"""Initialize and return the appropriate output buffer object Note that this just the
buffer object.
This does not actually connect the buffer object to the graph.
"""
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@classmethod
@abstractmethod
def reset_states(cls, config, randomize=False):
"""Randomize or reset the neuron states.
Parameters
----------
randomize (bool):
If true, the states will be set to random initial values. Else they will be set to zero
"""
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@classmethod
def set_all_v_mem_to_zeros(cls, samna_device, layer_id: int) -> None:
"""Reset all memory states to zeros.
Parameters
----------
samna_device:
samna device object to erase vmem memory.
layer_id:
layer index
"""
mod = cls.get_samna_module()
layer_constraint: LayerConstraints = cls.get_constraints()[layer_id]
events = []
for i in range(layer_constraint.neuron_memory):
event = mod.event.WriteNeuronValue()
event.address = i
event.layer = layer_id
event.neuron_state = 0
events.append(event)
temporary_source_node = cls.get_input_buffer()
temporary_graph = samna.graph.sequential(
[temporary_source_node, samna_device.get_model().get_sink_node()]
)
temporary_graph.start()
temporary_source_node.write(events)
temporary_graph.stop()
return
