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a2d6a0d | 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 | """classroom_memory.py — EWC y Replay Buffer para protección de memoria."""
from __future__ import annotations
import random
import time
from collections import deque
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
def compute_ewc_baseline(
model: nn.Module,
tokenizer: object,
ewc_lambda: float,
ewc_samples: int,
seq_len: int,
device: torch.device,
) -> "EWC":
"""Calcula Fisher Information sobre datos que el modelo ya maneja bien.
Returns:
Instancia de EWC con Fisher calculada.
"""
baseline_prompts = [
"def suma(a, b):",
"for i in range(10):",
"class Punto:",
"if x > 0:",
"import os\n",
"def fibonacci(n):",
"return sorted(",
"try:\n ",
"with open('",
"result = [x for x in",
]
baseline_tokens: list[torch.Tensor] = []
model.eval()
for prompt in baseline_prompts:
ids = tokenizer.Encode(prompt)
if len(ids) < 4:
continue
for _ in range(20):
if len(ids) > 2:
start = random.randint(0, max(0, len(ids) - 3))
chunk = ids[start : start + min(seq_len, len(ids) - start)]
baseline_tokens.append(torch.tensor(chunk, dtype=torch.long))
ewc = EWC(model, ewc_lambda)
if baseline_tokens:
ewc.compute_fisher(model, baseline_tokens, device, ewc_samples)
return ewc
class EWC:
"""
Elastic Weight Consolidation (Kirkpatrick et al., 2017).
Simula LTP biológica: identifica pesos importantes (alta Fisher info)
y penaliza moverlos durante entrenamiento nuevo.
L_total = L_task + (λ/2) * Σ F_i * (θ_i - θ*_i)²
"""
def __init__(self, model: nn.Module, lam: float = 500.0):
self.lam = lam
self.params_star: dict[str, torch.Tensor] = {}
self.fisher: dict[str, torch.Tensor] = {}
def compute_fisher(
self,
model: nn.Module,
data_loader: list[torch.Tensor],
device: torch.device,
n_samples: int = 200,
) -> None:
"""Calcula la Diagonal Fisher Information Matrix sobre datos existentes."""
model.eval()
self.params_star = {
n: p.data.clone() for n, p in model.named_parameters() if p.requires_grad
}
self.fisher = {
n: torch.zeros_like(p.data)
for n, p in model.named_parameters()
if p.requires_grad
}
n = min(n_samples, len(data_loader))
samples = random.sample(data_loader, n) if len(data_loader) > n else data_loader
for tokens in samples:
model.zero_grad()
tokens = tokens.to(device)
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
input_ids = tokens[:, :-1]
targets = tokens[:, 1:]
logits, _, _ = model(input_ids)
loss = F.cross_entropy(
logits.reshape(-1, logits.size(-1)),
targets.reshape(-1),
ignore_index=-100,
)
loss.backward()
for name, param in model.named_parameters():
if param.requires_grad and param.grad is not None:
self.fisher[name] += param.grad.data.pow(2) / n
model.zero_grad()
def penalty(self, model: nn.Module) -> torch.Tensor:
"""Calcula la penalización EWC: (λ/2) * Σ F_i * (θ_i - θ*_i)²"""
loss = torch.tensor(0.0, device=next(model.parameters()).device)
for name, param in model.named_parameters():
if name in self.fisher:
loss += (
self.fisher[name] * (param - self.params_star[name]).pow(2)
).sum()
return (self.lam / 2.0) * loss
class ReplayBuffer:
"""
Buffer circular de ejemplos exitosos.
Mezcla ejemplos nuevos con viejos para evitar olvido catastrófico.
Como el replay neuronal durante el sueño: reactiva memorias viejas
mientras integra las nuevas.
"""
def __init__(self, maxsize: int = 100):
self.buffer: deque[dict] = deque(maxlen=maxsize)
def add(
self,
problem: str,
solution: str,
input_ids: torch.Tensor,
labels: torch.Tensor,
level: int,
) -> None:
self.buffer.append(
{
"problem": problem,
"solution": solution,
"input_ids": input_ids.cpu(),
"labels": labels.cpu(),
"level": level,
"timestamp": time.time(),
}
)
def sample(self, n: int) -> list[dict]:
if len(self.buffer) == 0:
return []
n = min(n, len(self.buffer))
return random.sample(list(self.buffer), n)
def __len__(self) -> int:
return len(self.buffer)
@dataclass
class LessonResult:
"""Resultado de una lección."""
lesson_id: int
level: int
problem: str
student_answer: str
teacher_solution: str
correct: bool
feedback: str
loss: float
ewc_penalty: float
brain_score: float
timestamp: float = field(default_factory=time.time)
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