Merge branch 'main' into fix/lifRefractoryDynamics

.gitignore

@@ -145,3 +145,5 @@ dmypy.json
 .idea/
 .vscode/
 .history/
+.flakeheaven_cache/
+tutorials/in_depth/results/

src/lava/proc/atrlif/models.py

@@ -0,0 +1,267 @@
+# Copyright (C) 2024 Intel Corporation
+# Copyright (C) 2024 Jannik Luboeinski
+# SPDX-License-Identifier: BSD-3-Clause
+# See: https://spdx.org/licenses/
+
+import numpy as np
+from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol
+from lava.magma.core.model.py.ports import PyInPort, PyOutPort
+from lava.magma.core.model.py.type import LavaPyType
+from lava.magma.core.resources import CPU
+from lava.magma.core.decorator import implements, requires, tag
+from lava.magma.core.model.py.model import PyLoihiProcessModel
+
+from lava.proc.atrlif.process import ATRLIF
+
+
+@implements(proc=ATRLIF, protocol=LoihiProtocol)
+@requires(CPU)
+@tag("floating_pt")
+class PyATRLIFModelFloat(PyLoihiProcessModel):
+    """
+    Implementation of Adaptive Threshold and Refractoriness Leaky-Integrate-
+    and-Fire neuron process in floating-point precision. This short and simple
+    ProcessModel can be used for quick algorithmic prototyping, without
+    engaging with the nuances of a fixed-point implementation.
+    """
+    a_in: PyInPort = LavaPyType(PyInPort.VEC_DENSE, float)
+    s_out = None
+    i: np.ndarray = LavaPyType(np.ndarray, float)
+    v: np.ndarray = LavaPyType(np.ndarray, float)
+    theta: np.ndarray = LavaPyType(np.ndarray, float)
+    r: np.ndarray = LavaPyType(np.ndarray, float)
+    s: np.ndarray = LavaPyType(np.ndarray, bool)
+    bias_mant: np.ndarray = LavaPyType(np.ndarray, float)
+    bias_exp: np.ndarray = LavaPyType(np.ndarray, float)
+    delta_i: float = LavaPyType(float, float)
+    delta_v: float = LavaPyType(float, float)
+    delta_theta: float = LavaPyType(float, float)
+    delta_r: float = LavaPyType(float, float)
+    theta_0: float = LavaPyType(float, float)
+    theta_step: float = LavaPyType(float, float)
+    s_out: PyOutPort = LavaPyType(PyOutPort.VEC_DENSE, float)
+
+    def __init__(self, proc_params):
+        super(PyATRLIFModelFloat, self).__init__(proc_params)
+
+    def subthr_dynamics(self, activation_in: np.ndarray):
+        """
+        Sub-threshold dynamics for the model:
+          i[t] = (1-delta_i)*i[t-1] + x[t]
+          v[t] = (1-delta_v)*v[t-1] + i[t] + bias_mant
+          theta[t] = (1-delta_theta)*(theta[t-1] - theta_0) + theta_0
+          r[t] = (1-delta_r)*r[t-1]
+        """
+        self.i[:] = (1 - self.delta_i) * self.i + activation_in
+        self.v[:] = (1 - self.delta_v) * self.v + self.i + self.bias_mant
+        self.theta[:] = (1 - self.delta_theta) * (self.theta - self.theta_0) \
+            + self.theta_0
+        self.r[:] = (1 - self.delta_r) * self.r
+
+    def post_spike(self, spike_vector: np.ndarray):
+        """
+        Post spike/refractory behavior:
+          r[t] = r[t] + 2*theta[t]
+          theta[t] = theta[t] + theta_step
+        """
+        # For spiking neurons, set new values for refractory state and
+        # threshold
+        r_spiking = self.r[spike_vector]
+        theta_spiking = self.theta[spike_vector]
+        self.r[spike_vector] = r_spiking + 2 * theta_spiking
+        self.theta[spike_vector] = theta_spiking + self.theta_step
+
+    def run_spk(self):
+        """
+        The run function that performs the actual computation. Processes spike
+        events that occur if (v[t] - r[t]) >= theta[t].
+        """
+        # Receive synaptic input
+        a_in_data = self.a_in.recv()
+
+        # Perform the sub-threshold and spike computations
+        self.subthr_dynamics(activation_in=a_in_data)
+        self.s[:] = (self.v - self.r) >= self.theta
+        self.post_spike(spike_vector=self.s)
+        self.s_out.send(self.s)
+
+
+@implements(proc=ATRLIF, protocol=LoihiProtocol)
+@requires(CPU)
+@tag("bit_accurate_loihi", "fixed_pt")
+class PyATRLIFModelFixed(PyLoihiProcessModel):
+    """
+    Implementation of Adaptive Threshold and Refractoriness Leaky-Integrate-
+    and-Fire neuron process in fixed-point precision, bit-by-bit mimicking the
+    fixed-point computation behavior of Loihi 2.
+    """
+    a_in: PyInPort = LavaPyType(PyInPort.VEC_DENSE, np.int16, precision=16)
+    i: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24)
+    v: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24)
+    theta: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24)
+    r: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24)
+    s: np.ndarray = LavaPyType(np.ndarray, bool)
+    bias_mant: np.ndarray = LavaPyType(np.ndarray, np.int16, precision=13)
+    bias_exp: np.ndarray = LavaPyType(np.ndarray, np.int16, precision=3)
+    delta_i: int = LavaPyType(int, np.uint16, precision=12)
+    delta_v: int = LavaPyType(int, np.uint16, precision=12)
+    delta_theta: int = LavaPyType(int, np.uint16, precision=12)
+    delta_r: int = LavaPyType(int, np.uint16, precision=12)
+    theta_0: int = LavaPyType(int, np.uint16, precision=12)
+    theta_step: int = LavaPyType(int, np.uint16, precision=12)
+    s_out: PyOutPort = LavaPyType(PyOutPort.VEC_DENSE, np.int32, precision=24)
+
+    def __init__(self, proc_params):
+        super(PyATRLIFModelFixed, self).__init__(proc_params)
+
+        # The `ds_offset` constant enables setting decay constant values to
+        # exact 4096 = 2**12. Without it, the range of 12-bit unsigned
+        # `delta_i` is 0 to 4095.
+        self.ds_offset = 1
+        self.isthrscaled = False
+        self.effective_bias = 0
+        # State variables i and v are 24 bits wide
+        self.iv_bitwidth = 24
+        self.max_iv_val = 2**(self.iv_bitwidth - 1)
+        # Decays need an MSB alignment by 12 bits
+        self.decay_shift = 12
+        self.decay_unity = 2**self.decay_shift
+        # Threshold and incoming activation are MSB-aligned by 6 bits
+        self.theta_unity = 2**6
+        self.act_unity = 2**6
+
+    def subthr_dynamics(self, activation_in: np.ndarray):
+        """
+        Sub-threshold dynamics for the model:
+          i[t] = (1-delta_i)*i[t-1] + x[t]
+          v[t] = (1-delta_v)*v[t-1] + i[t] + bias_mant
+          theta[t] = (1-delta_theta)*(theta[t-1] - theta_0) + theta_0
+          r[t] = (1-delta_r)*r[t-1]
+        """
+        # Update current
+        # --------------
+        # Multiplication is done for left shifting, offset is added
+        decay_const_i = self.delta_i * self.decay_unity + self.ds_offset
+        # Below, i is promoted to int64 to avoid overflow of the product
+        # between i and decay constant beyond int32.
+        # Subsequent right shift by 12 brings us back within 24-bits (and
+        # hence, within 32-bits).
+        i_decayed = np.int64(self.i * (self.decay_unity - decay_const_i))
+        i_decayed = np.sign(i_decayed) * np.right_shift(
+            np.abs(i_decayed), self.decay_shift
+        )
+        # Multiplication is done for left shifting (to account for MSB
+        # alignment done by the hardware).
+        activation_in = activation_in * self.act_unity
+        # Add synaptic input to decayed current
+        i_updated = np.int32(i_decayed + activation_in)
+        # Check if value of current is within bounds of 24-bit. Overflows are
+        # handled by wrapping around modulo.
+        # 2 ** 23. E.g., (2 ** 23) + k becomes k and -(2**23 + k) becomes -k
+        wrapped_curr = np.where(
+            i_updated > self.max_iv_val,
+            i_updated - 2 * self.max_iv_val,
+            i_updated,
+        )
+        wrapped_curr = np.where(
+            wrapped_curr <= -self.max_iv_val,
+            i_updated + 2 * self.max_iv_val,
+            wrapped_curr,
+        )
+        self.i[:] = wrapped_curr
+
+        # Update voltage (proceeding similar to current update)
+        # -----------------------------------------------------
+        decay_const_v = self.delta_v * self.decay_unity
+        neg_voltage_limit = -np.int32(self.max_iv_val) + 1
+        pos_voltage_limit = np.int32(self.max_iv_val) - 1
+        v_decayed = np.int64(self.v) * np.int64(self.decay_unity
+                                                - decay_const_v)
+        v_decayed = np.sign(v_decayed) * np.right_shift(
+            np.abs(v_decayed), self.decay_shift
+        )
+        v_updated = np.int32(v_decayed + self.i + self.effective_bias)
+        self.v[:] = np.clip(v_updated, neg_voltage_limit, pos_voltage_limit)
+
+        # Update threshold (proceeding similar to current update)
+        # -------------------------------------------------------
+        decay_const_theta = self.delta_theta * self.decay_unity
+        theta_diff_decayed = np.int64(self.theta - self.theta_0) * \
+            np.int64(self.decay_unity - decay_const_theta)
+        theta_diff_decayed = np.sign(theta_diff_decayed) * np.right_shift(
+            np.abs(theta_diff_decayed), self.decay_shift
+        )
+        self.theta[:] = np.int32(theta_diff_decayed) + self.theta_0
+        # TODO do clipping here?
+
+        # Update refractoriness (decaying similar to current)
+        # ---------------------------------------------------
+        decay_const_r = self.delta_r * self.decay_unity
+        r_decayed = np.int64(self.r) * np.int64(self.decay_unity
+                                                - decay_const_r)
+        r_decayed = np.sign(r_decayed) * np.right_shift(
+            np.abs(r_decayed), self.decay_shift
+        )
+        self.r[:] = np.int32(r_decayed)
+        # TODO do clipping here?
+
+    def scale_bias(self):
+        """
+        Scale bias with bias exponent by taking into account sign of the
+        exponent.
+        """
+        # Create local copy of `bias_mant` with promoted dtype to prevent
+        # overflow when applying shift of `bias_exp`.
+        bias_mant = self.bias_mant.copy().astype(np.int32)
+        self.effective_bias = np.where(
+            self.bias_exp >= 0,
+            np.left_shift(bias_mant, self.bias_exp),
+            np.right_shift(bias_mant, -self.bias_exp),
+        )
+
+    def scale_threshold(self):
+        """
+        Scale threshold according to the way Loihi hardware scales it. In Loihi
+        hardware, threshold is left-shifted by 6-bits to MSB-align it with
+        other state variables of higher precision.
+        """
+        # Multiplication is done for left shifting
+        self.theta_0 = np.int32(self.theta_0 * self.theta_unity)
+        self.theta = np.full(self.theta.shape, self.theta_0)
+        self.theta_step = np.int32(self.theta_step * self.theta_unity)
+        self.isthrscaled = True
+
+    def post_spike(self, spike_vector: np.ndarray):
+        """
+        Post spike/refractory behavior:
+          r[t] = r[t] + 2*theta[t]
+          theta[t] = theta[t] + theta_step
+        """
+        # For spiking neurons, set new values for refractory state and
+        # threshold.
+        r_spiking = self.r[spike_vector]
+        theta_spiking = self.theta[spike_vector]
+        self.r[spike_vector] = r_spiking + 2 * theta_spiking
+        self.theta[spike_vector] = theta_spiking + self.theta_step
+
+    def run_spk(self):
+        """
+        The run function that performs the actual computation. Processes spike
+        events that occur if (v[t] - r[t]) >= theta[t].
+        """
+        # Receive synaptic input
+        a_in_data = self.a_in.recv()
+
+        # Compute effective bias
+        self.scale_bias()
+
+        # Compute scaled threshold-related variables only once, not every
+        # timestep (has to be done once after object construction).
+        if not self.isthrscaled:
+            self.scale_threshold()
+
+        # Perform the sub-threshold and spike computations
+        self.subthr_dynamics(activation_in=a_in_data)
+        self.s[:] = (self.v - self.r) >= self.theta
+        self.post_spike(spike_vector=self.s)
+        self.s_out.send(self.s)