SigmaS4Delta Neuronmodel and Layer with Unittests

src/lava/proc/s4d/models.py

@@ -0,0 +1,169 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: BSD-3-Clause
+# See: https://spdx.org/licenses/
+
+import numpy as np
+from typing import Any, Dict
+from lava.proc.sdn.models import AbstractSigmaDeltaModel
+from lava.magma.core.decorator import implements, requires, tag
+from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol
+from lava.proc.s4d.process import SigmaS4dDelta, SigmaS4dDeltaLayer
+from lava.magma.core.resources import CPU
+from lava.magma.core.model.py.ports import PyInPort, PyOutPort
+from lava.magma.core.model.py.type import LavaPyType
+from lava.magma.core.model.sub.model import AbstractSubProcessModel
+from lava.proc.sparse.process import Sparse
+
+
+class AbstractSigmaS4dDeltaModel(AbstractSigmaDeltaModel):
+    a_in = None
+    s_out = None
+
+    # SigmaDelta Variables
+    vth = None
+    sigma = None
+    act = None
+    residue = None
+    error = None
+    state_exp = None
+    bias = None
+
+    # S4 Variables
+    a = None
+    b = None
+    c = None
+    s4_state = None
+    s4_exp = None
+
+    def __init__(self, proc_params: Dict[str, Any]) -> None:
+        """
+        Sigma delta neuron model that implements S4D
+        (as described by Gu et al., 2022) dynamics as its activation function.
+
+        Relevant parameters in proc_params
+        --------------------------
+        a: np.ndarray
+            Diagonal elements of the state matrix of the S4D model.
+        b: np.ndarray
+            Diagonal elements of the input matrix of the S4D model.
+        c: np.ndarray
+            Diagonal elements of the output matrix of the S4D model.
+        s4_state: np.ndarray
+            State vector of the S4D model.
+        """
+        super().__init__(proc_params)
+        self.a = self.proc_params['a']
+        self.b = self.proc_params['b']
+        self.c = self.proc_params['c']
+        self.s4_state = self.proc_params['s4_state']
+
+    def activation_dynamics(self, sigma_data: np.ndarray) -> np.ndarray:
+        """Sigma Delta activation dynamics. Performs S4D dynamics.
+
+        This function simulates the behavior of a linear time-invariant system
+        with diagonalized state-space representation.
+        (For reference see Gu et al., 2022)
+
+        The state-space equations are given by:
+        s4_state_{k+1} = A * s4_state_k + B * input_k
+        act_k = C * s4_state_k
+
+        where:
+        - s4_state_k is the state vector at time step k,
+        - input_k is the input vector at time step k,
+        - act_k is the output vector at time step k,
+        - A is the diagonal state matrix,
+        - B is the diagonal input matrix,
+        - C is the diagonal output matrix.
+
+        The function computes the next output step of the
+        system for the given input signal.
+
+        Parameters
+        ----------
+        sigma_data: np.ndarray
+            sigma decoded data
+
+        Returns
+        -------
+        np.ndarray
+            activation output
+        """
+
+        self.s4_state = self.s4_state * self.a + sigma_data * self.b
+        act = self.c * self.s4_state * 2
+        return act
+
+
+@implements(proc=SigmaS4dDelta, protocol=LoihiProtocol)
+@requires(CPU)
+@tag('floating_pt')
+class PySigmaS4dDeltaModelFloat(AbstractSigmaS4dDeltaModel):
+    """Floating point implementation of SigmaS4dDelta neuron."""
+    a_in = LavaPyType(PyInPort.VEC_DENSE, float)
+    s_out = LavaPyType(PyOutPort.VEC_DENSE, float)
+
+    vth: np.ndarray = LavaPyType(np.ndarray, float)
+    sigma: np.ndarray = LavaPyType(np.ndarray, float)
+    act: np.ndarray = LavaPyType(np.ndarray, float)
+    residue: np.ndarray = LavaPyType(np.ndarray, float)
+    error: np.ndarray = LavaPyType(np.ndarray, float)
+    bias: np.ndarray = LavaPyType(np.ndarray, float)
+
+    state_exp: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=3)
+    cum_error: np.ndarray = LavaPyType(np.ndarray, bool, precision=1)
+    spike_exp: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=3)
+    s4_exp: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=3)
+
+    # S4 vaiables
+    s4_state: np.ndarray = LavaPyType(np.ndarray, float)
+    a: np.ndarray = LavaPyType(np.ndarray, float)
+    b: np.ndarray = LavaPyType(np.ndarray, float)
+    c: np.ndarray = LavaPyType(np.ndarray, float)
+
+    def run_spk(self) -> None:
+        # Receive synaptic input
+        a_in_data = self.a_in.recv()
+        s_out = self.dynamics(a_in_data)
+        self.s_out.send(s_out)
+
+
+@implements(proc=SigmaS4dDeltaLayer, protocol=LoihiProtocol)
+class SubDenseLayerModel(AbstractSubProcessModel):
+    def __init__(self, proc):
+        """Builds (Sparse -> S4D -> Sparse) connection of the process."""
+        conn_weights = proc.proc_params.get("conn_weights")
+        shape = proc.proc_params.get("shape")
+        state_exp = proc.proc_params.get("state_exp")
+        num_message_bits = proc.proc_params.get("num_message_bits")
+        s4_exp = proc.proc_params.get("s4_exp")
+        d_states = proc.proc_params.get("d_states")
+        a = proc.proc_params.get("a")
+        b = proc.proc_params.get("b")
+        c = proc.proc_params.get("c")
+        vth = proc.proc_params.get("vth")
+
+        # Instantiate processes
+        self.sparse1 = Sparse(weights=conn_weights.T, weight_exp=state_exp,
+                              num_message_bits=num_message_bits)
+        self.sigma_S4d_delta = SigmaS4dDelta(shape=(shape[0] * d_states,),
+                                             vth=vth,
+                                             state_exp=state_exp,
+                                             s4_exp=s4_exp,
+                                             a=a,
+                                             b=b,
+                                             c=c)
+        self.sparse2 = Sparse(weights=conn_weights, weight_exp=state_exp,
+                              num_message_bits=num_message_bits)
+
+        # Make connections Sparse -> SigmaS4Delta -> Sparse
+        proc.in_ports.s_in.connect(self.sparse1.in_ports.s_in)
+        self.sparse1.out_ports.a_out.connect(self.sigma_S4d_delta.in_ports.a_in)
+        self.sigma_S4d_delta.out_ports.s_out.connect(self.sparse2.s_in)
+        self.sparse2.out_ports.a_out.connect(proc.out_ports.a_out)
+
+        # Set aliases
+        proc.vars.a.alias(self.sigma_S4d_delta.vars.a)
+        proc.vars.b.alias(self.sigma_S4d_delta.vars.b)
+        proc.vars.c.alias(self.sigma_S4d_delta.vars.c)
+        proc.vars.s4_state.alias(self.sigma_S4d_delta.vars.s4_state)

src/lava/proc/s4d/process.py

@@ -0,0 +1,167 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: BSD-3-Clause
+# See: https://spdx.org/licenses/
+
+import typing as ty
+import numpy as np
+from lava.magma.core.process.process import AbstractProcess
+from lava.magma.core.process.variable import Var
+from lava.magma.core.process.ports.ports import InPort, OutPort
+from lava.proc.sdn.process import ActivationMode, SigmaDelta
+
+
+class SigmaS4dDelta(SigmaDelta, AbstractProcess):
+    def __init__(
+            self,
+            shape: ty.Tuple[int, ...],
+            vth: ty.Union[int, float],
+            a: float,
+            b: float,
+            c: float,
+            state_exp: ty.Optional[int] = 0,
+            s4_exp: ty.Optional[int] = 0) -> None:
+        """
+        Sigma delta neuron process that implements S4D (described by
+        Gu et al., 2022) dynamics as its activation function.
+
+        This process simulates the behavior of a linear time-invariant system
+        with diagonal state-space representation.
+        The state-space equations are given by:
+        s4_state_{k+1} = A * s4_state_k + B * inp_k
+        act_k = C * s4_state_k
+
+        where:
+        - s4_state_k is the state vector at time step k,
+        - inp_k is the input vector at time step k,
+        - act_k is the output vector at time step k,
+        - A is the diagonal state matrix,
+        - B is the diagonal input matrix,
+        - C is the diagonal output matrix.
+
+        Parameters
+        ----------
+        shape: Tuple
+            Shape of the sigma process.
+        vth: int or float
+            Threshold of the delta encoder.
+        a: np.ndarray
+            Diagonal elements of the state matrix of the S4D model.
+        b: np.ndarray
+            Diagonal elements of the input matrix of the S4D model.
+        c: np.ndarray
+            Diagonal elements of the output matrix of the S4D model.
+        state_exp: int
+            Scaling exponent with base 2 for the reconstructed sigma variables.
+            Note: This should only be used for nc models.
+            Default is 0.
+        s4_exp: int
+            Scaling exponent with base 2 for the S4 state variables.
+            Note: This should only be used for nc models.
+            Default is 0.
+        """
+
+        super().__init__(shape=shape,
+                         vth=vth,
+                         a=a,
+                         b=b,
+                         c=c,
+                         s4_state=0,
+                         state_exp=state_exp,
+                         s4_exp=s4_exp)
+
+        # Variables for S4
+        self.a = Var(shape=shape, init=a)
+        self.b = Var(shape=shape, init=b)
+        self.c = Var(shape=shape, init=c)
+        self.s4_state = Var(shape=shape, init=0)
+        self.s4_exp = Var(shape=(1,), init=s4_exp)
+
+
+class SigmaS4dDeltaLayer(AbstractProcess):
+    def __init__(
+            self,
+            shape: ty.Tuple[int, ...],
+            vth: ty.Union[int, float],
+            a: float,
+            b: float,
+            c: float,
+            d_states: ty.Optional[int] = 1,
+            s4_exp: ty.Optional[int] = 0,
+            num_message_bits: ty.Optional[int] = 24,
+            state_exp: ty.Optional[int] = 0) -> None:
+        """
+        Combines S4D neuron with Sparse Processes that allow for multiple
+        d_states.
+
+        Connectivity: Sparse -> SigmaS4dDelta -> Sparse.
+        Relieves user from computing required connection weights for multiple
+        d_states.
+
+        Parameters
+        ----------
+        shape: Tuple
+            Shape of the sigma process.
+        vth: int or float
+            Threshold of the delta encoder.
+        a: np.ndarray
+            Diagonal elements of the state matrix of the S4D model.
+        b: np.ndarray
+            Diagonal elements of the input matrix of the S4D model.
+        c: np.ndarray
+            Diagonal elements of the output matrix of the S4D model.
+        d_states: int
+            Number of hidden states of the S4D model.
+            Default is 1.
+        state_exp: int
+            Scaling exponent with base 2 for the reconstructed sigma variables.
+            Note: Only relevant for nc model.
+            Default is 0.
+        num_message_bits: int
+            Number of message bits to be used in Sparse connection processes.
+            Note: Only relevant for nc model.
+        s4_exp: int
+            Scaling exponent with base 2 for the S4 state variables.
+            Note: Only relevant for nc model.
+            Default is 0.
+        """
+
+        # Automatically takes care of expansion and reduction of dimensionality
+        # for multiple hidden states (d_states)
+        conn_weights = np.kron(np.eye(shape[0]), np.ones(d_states))
+        s4_state = 0
+        super().__init__(shape=shape,
+                         vth=vth,
+                         a=a,
+                         b=b,
+                         c=c,
+                         s4_exp=s4_exp,
+                         s4_state=s4_state,
+                         conn_weights=conn_weights,
+                         num_message_bits=num_message_bits,
+                         d_states=d_states,
+                         state_exp=state_exp,
+                         act_mode=ActivationMode.UNIT)
+
+        # Ports
+        self.s_in = InPort(shape=shape)
+        self.a_out = OutPort(shape=shape)
+
+        # General variables
+        self.state_exp = Var(shape=(1,), init=state_exp)
+
+        # Variables for S4
+        self.a = Var(shape=(shape[0] * d_states,), init=a)
+        self.b = Var(shape=(shape[0] * d_states,), init=b)
+        self.c = Var(shape=(shape[0] * d_states,), init=c)
+        self.s4_state = Var(shape=(shape[0] * d_states,), init=0)
+        self.S4_exp = Var(shape=(1,), init=s4_exp)
+
+        # Variables for connecting Dense processes
+        # Project input_dim to input_dim * d_states
+        self.conn_weights = Var(shape=shape, init=conn_weights)
+        self.num_message_bits = Var(shape=(1,), init=num_message_bits)
+
+    @property
+    def shape(self) -> ty.Tuple[int, ...]:
+        """Return shape of the Process."""
+        return self.proc_params['shape']

src/lava/proc/sdn/process.py

@@ -126,7 +126,8 @@ def __init__(
             act_mode: ty.Optional[ActivationMode] = ActivationMode.RELU,
             cum_error: ty.Optional[bool] = False,
             spike_exp: ty.Optional[int] = 0,
-            state_exp: ty.Optional[int] = 0) -> None:
+            state_exp: ty.Optional[int] = 0,
+            **kwargs) -> None:
         """Sigma delta neuron process. At the moment only ReLu activation is
         supported. Spike mechanism based on accumulated error is also supported.
 
@@ -173,7 +174,7 @@ def __init__(
         """
         super().__init__(shape=shape, vth=vth, bias=bias,
                          act_mode=act_mode, cum_error=cum_error,
-                         spike_exp=spike_exp, state_exp=state_exp)
+                         spike_exp=spike_exp, state_exp=state_exp, **kwargs)
         # scaling factor for fixed precision scaling
         vth = vth * (1 << (spike_exp + state_exp))
         bias = bias * (1 << (spike_exp + state_exp))