File size: 6,175 Bytes
28c5d43 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | """
Unified LoRA Controller
========================
Adaptive parameter-efficient fine-tuning controller with automatic
Single/Multi/Mirror mode switching based on synaptic stress signals.
Author: Simona Vargiu
License: Apache 2.0
"""
import torch
from typing import Dict, Optional, Tuple
class UnifiedController:
"""
Unified LoRA adaptive controller.
Monitors training stress via synaptic signal φ(t) and automatically
switches between three operational modes:
- Mode 0 (Single): Shared adapter for low conflict
- Mode 1 (Multi): Task-specific adapters for moderate stress
- Mode 2 (Mirror): Stability snapshots for catastrophic forgetting
Args:
alpha (float): Learning rate for φ(t) updates (default: 0.1)
beta (float): EMA smoothing factor for loss (default: 0.9)
theta0 (float): Single/Multi threshold (default: 0.3)
theta1 (float): Multi/Mirror threshold (default: 0.7)
lr_single (float): Learning rate for Single mode (default: 5e-5)
lr_multi (float): Learning rate for Multi mode (default: 3e-5)
lr_mirror (float): Learning rate for Mirror mode (default: 1e-5)
Example:
>>> controller = UnifiedController()
>>> for step, batch in enumerate(train_loader):
... outputs = model(**batch)
... new_lr = controller.update(outputs.loss.item())
... # Apply new_lr to optimizer
"""
def __init__(
self,
alpha: float = 0.1,
beta: float = 0.9,
theta0: float = 0.3,
theta1: float = 0.7,
lr_single: float = 5e-5,
lr_multi: float = 3e-5,
lr_mirror: float = 1e-5,
):
self.alpha = alpha
self.beta = beta
self.theta0 = theta0
self.theta1 = theta1
# Learning rates per mode
self.lr_map = {
0: lr_single,
1: lr_multi,
2: lr_mirror,
}
# State variables
self.phi = 0.5 # Synaptic stress signal
self.E_smooth = 1.0 # Smoothed loss
self.mode = 1 # Current mode (start with Multi)
self.step = 0
# History tracking
self.history = {
"phi": [],
"E_smooth": [],
"mode": [],
"step": [],
}
def update(self, loss: float) -> float:
"""
Update controller state and return new learning rate.
Args:
loss (float): Current training loss
Returns:
float: New learning rate based on current mode
"""
self.step += 1
# Update smoothed loss (EMA)
E = float(loss)
self.E_smooth = self.beta * self.E_smooth + (1 - self.beta) * E
# Compute normalized stress signal
D = self.E_smooth / (1 + self.E_smooth) # Normalize to [0,1]
# Update synaptic signal φ(t) with EMA
self.phi = (1 - self.alpha) * self.phi + self.alpha * D
# FSM: Determine mode based on φ(t)
if self.phi < self.theta0:
self.mode = 0 # Single
elif self.phi < self.theta1:
self.mode = 1 # Multi
else:
self.mode = 2 # Mirror
# Log history
self.history["phi"].append(self.phi)
self.history["E_smooth"].append(self.E_smooth)
self.history["mode"].append(self.mode)
self.history["step"].append(self.step)
# Return learning rate for current mode
return self.lr_map[self.mode]
def get_state(self) -> Dict[str, float]:
"""
Get current controller state.
Returns:
dict: Current values of phi, E_smooth, mode, step
"""
return {
"phi": self.phi,
"E_smooth": self.E_smooth,
"mode": self.mode,
"step": self.step,
}
def get_history(self) -> Dict[str, list]:
"""
Get complete training history.
Returns:
dict: History of phi, E_smooth, mode, step
"""
return self.history
def reset(self):
"""Reset controller to initial state."""
self.phi = 0.5
self.E_smooth = 1.0
self.mode = 1
self.step = 0
self.history = {
"phi": [],
"E_smooth": [],
"mode": [],
"step": [],
}
@staticmethod
def mode_name(mode: int) -> str:
"""
Get human-readable mode name.
Args:
mode (int): Mode number (0, 1, or 2)
Returns:
str: Mode name
"""
names = {0: "Single", 1: "Multi", 2: "Mirror"}
return names.get(mode, "Unknown")
def __repr__(self) -> str:
"""String representation of controller state."""
return (
f"UnifiedController(step={self.step}, phi={self.phi:.3f}, "
f"mode={self.mode} ({self.mode_name(self.mode)}), "
f"E_smooth={self.E_smooth:.3f})"
)
# Example usage
if __name__ == "__main__":
import numpy as np
print("Unified LoRA Controller - Example")
print("=" * 50)
controller = UnifiedController()
# Simulate training with stress events
print("\nSimulating training with SHOCK at step 150...")
print()
for step in range(300):
# Simulate loss
if step < 150:
loss = np.random.uniform(0.4, 0.6) # Normal training
else:
loss = np.random.uniform(2.0, 4.0) # SHOCK
# Update controller
new_lr = controller.update(loss)
# Log every 50 steps
if step % 50 == 0:
state = controller.get_state()
print(
f"[{step:3d}] phi={state['phi']:.3f} | "
f"mode={state['mode']} ({controller.mode_name(state['mode'])}) | "
f"lr={new_lr:.1e}"
)
print("\n" + "=" * 50)
print("Simulation complete!")
print(f"\nFinal state: {controller}")
|