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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from collections import deque |
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from typing import Deque, Dict, Any |
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class CognitiveMemory(nn.Module): |
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"""Differentiable memory system with biological consolidation mechanisms""" |
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def __init__(self, context_size: int, capacity: int = 100): |
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super().__init__() |
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self.context_size = context_size |
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self.capacity = capacity |
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self.memory_queue: Deque[Dict[str, Any]] = deque(maxlen=capacity) |
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self.key_proj = nn.Linear(context_size, 64) |
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self.value_proj = nn.Linear(context_size, 64) |
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self.importance_decay = nn.Parameter(torch.tensor(0.95)) |
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self.consolidation_threshold = 0.7 |
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self.age_decay = 0.1 |
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def add_memory(self, context: torch.Tensor, activation: float): |
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"""Store memory with dynamic importance weighting""" |
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importance = torch.sigmoid(torch.tensor(activation * 0.5 + 0.2)) |
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self.memory_queue.append({ |
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'context': context.detach().clone(), |
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'importance': importance, |
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'age': torch.tensor(0.0) |
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}) |
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def consolidate_memories(self): |
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"""Memory optimization through importance-based pruning""" |
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new_queue = deque(maxlen=self.capacity) |
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for mem in self.memory_queue: |
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mem['importance'] *= self.importance_decay |
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mem['age'] += self.age_decay |
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if mem['importance'] > 0.2: |
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new_queue.append(mem) |
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self.memory_queue = new_queue |
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def retrieve(self, query: torch.Tensor) -> torch.Tensor: |
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"""Content-based memory retrieval with attention""" |
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if not self.memory_queue: |
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return torch.zeros(64, device=query.device) |
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contexts = torch.stack([m['context'] for m in self.memory_queue]) |
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keys = self.key_proj(contexts) |
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values = self.value_proj(contexts) |
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query_proj = self.key_proj(query.unsqueeze(0)) |
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scores = F.softmax(keys @ query_proj.T, dim=0) |
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return (scores * values).sum(dim=0) |
