Fam Zheng
d923aa1e31
Hopfield + Hebbian hybrid memory system for LLMs. Two nights of experiments (16 iterations), validated on LongMemEval (ICLR 2025). Architecture: - Single-hop: Two-Stage Hopfield (NN top-20 → softmax settle) - Multi-hop: Hebbian W matrix with WTA pattern separation - 64% on LongMemEval (500 questions), retrieval-only, no LLM dependency - 4ms latency @ 20K memories, ~1GB VRAM Key findings: - Hopfield attention solved noise tolerance (20% → 100% vs flat Hebbian) - WTA pattern separation enables 20K+ capacity - Multi-hop associative chains (6 hops, CosSim=1.0) — RAG can't do this - MiniLM-L6 is optimal (discrimination gap > absolute similarity) - Paraphrase cue augmentation: 55% → 100% on synthetic, 36% → 64% on benchmark - SNN encoder viable (CosSim 0.99) but not needed for current architecture
261 lines
11 KiB
Python
261 lines
11 KiB
Python
"""Experiment 7e: Cue augmentation to overcome embedding model limitations.
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Idea: When storing a memory, also store augmented versions of the cue.
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If the user says "The database is slow", also store:
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- The embedding with added noise (gaussian augmentation)
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- A shifted version toward common paraphrase patterns
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This increases the "catchment basin" of each memory without changing the model.
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Also test: using the LLM itself to generate paraphrases (simulated here).
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"""
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import sys
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import time
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from pathlib import Path
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import torch
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import torch.nn as nn
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import numpy as np
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DEVICE = "cuda"
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def cosine(a, b):
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return nn.functional.cosine_similarity(a.unsqueeze(0), b.unsqueeze(0)).item()
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class AugmentedHopfield:
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"""Hopfield with cue augmentation.
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Each memory stores N augmented cue embeddings, all pointing to the same target.
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During recall, any of the augmented cues can match.
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"""
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def __init__(self, beta=16.0, top_k=20, n_augments=5, noise_std=0.15):
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self.beta = beta
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self.top_k = top_k
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self.n_augments = n_augments
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self.noise_std = noise_std
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self.cue_embs = []
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self.target_embs = []
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self.memory_ids = [] # Which original memory each entry belongs to
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def learn(self, cue_emb, target_emb, memory_id=None):
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"""Store with augmented cues."""
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mid = memory_id if memory_id is not None else len(set(self.memory_ids))
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# Original
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self.cue_embs.append(cue_emb.detach())
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self.target_embs.append(target_emb.detach())
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self.memory_ids.append(mid)
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# Augmented: add noise and renormalize
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for _ in range(self.n_augments):
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noisy = cue_emb + torch.randn_like(cue_emb) * self.noise_std
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noisy = nn.functional.normalize(noisy, dim=0)
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self.cue_embs.append(noisy)
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self.target_embs.append(target_emb.detach())
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self.memory_ids.append(mid)
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def learn_with_paraphrases(self, cue_embs_list, target_emb, memory_id=None):
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"""Store multiple cue embeddings for the same target.
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cue_embs_list: list of embeddings (original + paraphrases)
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"""
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mid = memory_id if memory_id is not None else len(set(self.memory_ids))
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for ce in cue_embs_list:
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self.cue_embs.append(ce.detach())
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self.target_embs.append(target_emb.detach())
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self.memory_ids.append(mid)
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def recall(self, query_emb, steps=3):
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cue_mat = torch.stack(self.cue_embs)
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target_mat = torch.stack(self.target_embs)
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N = cue_mat.shape[0]
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# Stage 1: top-K
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k = min(self.top_k, N)
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sims = query_emb @ cue_mat.T
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_, top_idx = sims.topk(k)
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cand_cues = cue_mat[top_idx]
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cand_targets = target_mat[top_idx]
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# Stage 2: Hopfield settle
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xi = query_emb
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for _ in range(steps):
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scores = self.beta * (xi @ cand_cues.T)
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attn = torch.softmax(scores, dim=0)
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xi = attn @ cand_cues
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xi = nn.functional.normalize(xi, dim=0)
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scores = self.beta * (xi @ cand_cues.T)
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attn = torch.softmax(scores, dim=0)
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target = attn @ cand_targets
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return nn.functional.normalize(target, dim=0)
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def load_model():
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from sentence_transformers import SentenceTransformer
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return SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
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def test_augmentation(model):
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"""Compare: no augmentation vs noise augmentation vs paraphrase augmentation."""
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print("\n=== Augmentation Comparison (20 pairs, 2000 bg) ===")
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pairs = [
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("What's the weather like today?", "User checks weather every morning"),
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("Let's deploy the new version", "Deployment uses GitHub Actions with k3s"),
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("The database is slow again", "Missing index on users table"),
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("I need to fix the authentication bug", "JWT tokens with 24h expiry in Redis"),
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("The API returns 500 errors", "OOM in the Python worker"),
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("Let's set up monitoring", "Prometheus + Grafana on OCI"),
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("Tests failing in CI", "CI needs postgres service container"),
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("Memory usage too high", "Leak in websocket handler"),
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("Help with Docker setup", "docker-compose for dev, k3s for prod"),
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("Log files too large", "Logs rotate daily, shipped to Loki"),
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("How to add caching?", "Redis available at redis.internal:6379"),
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("Frontend loads slowly", "CDN CloudFlare, 1h TTL for assets"),
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("Refactor payment module", "Stripe API, webhook in payments/webhook.py"),
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("Set up new server", "Ubuntu 22.04, Docker, Tailscale, monitoring"),
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("Optimize search", "Elasticsearch v8, recently upgraded"),
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("Backup the database", "Daily 3am UTC cron to S3"),
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("Configure reverse proxy", "Traefik, not nginx"),
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("Team meeting schedule", "Standup 10am London, Mon-Fri"),
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("Learn a new programming language", "User has Python+Go, new to systems"),
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("Review my pull request", "User prefers small PRs with clear commits"),
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]
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paraphrases = [
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"How's the weather?", "Ship the release", "DB performance terrible",
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"Fix the login issue", "Server errors everywhere", "Need observability",
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"CI tests breaking", "Service using too much RAM", "Docker config help",
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"Logs eating disk space", "Want to add a cache layer", "Website too slow",
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"Payment code needs rework", "Provision a new machine", "Search is slow",
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"Need a DB backup", "Proxy configuration", "When's the standup?",
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"Want to learn Rust", "Check my pull request",
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]
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# Hand-crafted additional paraphrases for hard cases
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extra_paraphrases = {
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1: ["Ship the release", "Push to production", "Release the new build"],
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3: ["Fix the login issue", "Authentication is broken", "Login doesn't work"],
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4: ["Server errors everywhere", "Getting 500s", "Internal server error"],
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5: ["Need observability", "Set up alerts", "Monitor the services"],
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10: ["Add a cache layer", "Implement caching", "Cache the responses"],
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11: ["Website too slow", "Page load time is bad", "Frontend performance"],
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13: ["Provision a new machine", "Need a new server", "Set up a new box"],
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17: ["When's the standup?", "What time is the meeting?", "Daily sync time?"],
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18: ["Want to learn Rust", "Getting into Rust", "Start learning Rust"],
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19: ["Check my pull request", "Look at my code changes", "PR review please"],
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}
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cue_embs = model.encode([p[0] for p in pairs], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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target_embs = model.encode([p[1] for p in pairs], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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para_embs = model.encode(paraphrases, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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# Encode extra paraphrases
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extra_embs = {}
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for idx, texts in extra_paraphrases.items():
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extra_embs[idx] = model.encode(texts, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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# Background
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topics = ["server", "database", "API", "frontend", "backend", "cache",
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"queue", "network", "storage", "auth", "docker", "kubernetes",
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"redis", "nginx", "postgres", "python", "golang", "react",
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"terraform", "ansible"]
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actions = ["crashed", "is slow", "needs update", "has bug", "timed out",
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"needs migration", "needs backup", "has leak", "is down", "needs config"]
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bg_cues = [f"The {topics[i%len(topics)]} {actions[i%len(actions)]} (ticket {i})"
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for i in range(2000)]
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bg_targets = [f"Fix {topics[i%len(topics)]} {actions[i%len(actions)]}: wiki {i}"
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for i in range(2000)]
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bg_cue_embs = model.encode(bg_cues, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE, batch_size=256)
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bg_target_embs = model.encode(bg_targets, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE, batch_size=256)
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def evaluate(mem, label):
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correct = 0
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for i in range(len(paraphrases)):
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with torch.no_grad():
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recalled = mem.recall(para_embs[i])
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all_sims = [cosine(recalled, target_embs[j]) for j in range(len(pairs))]
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if np.argmax(all_sims) == i:
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correct += 1
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n = len(paraphrases)
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print(f" {label}: {correct}/{n} ({correct/n:.0%})")
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return correct / n
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# Method 1: No augmentation (baseline)
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mem1 = AugmentedHopfield(n_augments=0)
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for i in range(len(pairs)):
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mem1.learn(cue_embs[i], target_embs[i], memory_id=i)
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for i in range(2000):
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mem1.learn(bg_cue_embs[i], bg_target_embs[i], memory_id=100+i)
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evaluate(mem1, "No augmentation")
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# Method 2: Noise augmentation (5 copies)
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for noise in [0.1, 0.15, 0.2, 0.3]:
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mem2 = AugmentedHopfield(n_augments=5, noise_std=noise)
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for i in range(len(pairs)):
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mem2.learn(cue_embs[i], target_embs[i], memory_id=i)
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for i in range(2000):
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# Don't augment background
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mem2.cue_embs.append(bg_cue_embs[i])
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mem2.target_embs.append(bg_target_embs[i])
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mem2.memory_ids.append(100+i)
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evaluate(mem2, f"Noise aug (σ={noise}, n=5)")
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# Method 3: Noise augmentation (20 copies)
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mem3 = AugmentedHopfield(n_augments=20, noise_std=0.15)
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for i in range(len(pairs)):
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mem3.learn(cue_embs[i], target_embs[i], memory_id=i)
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for i in range(2000):
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mem3.cue_embs.append(bg_cue_embs[i])
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mem3.target_embs.append(bg_target_embs[i])
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mem3.memory_ids.append(100+i)
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evaluate(mem3, "Noise aug (σ=0.15, n=20)")
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# Method 4: Paraphrase augmentation (hand-crafted extras)
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mem4 = AugmentedHopfield(n_augments=0)
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for i in range(len(pairs)):
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cue_list = [cue_embs[i]]
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if i in extra_embs:
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cue_list.extend([e for e in extra_embs[i]])
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mem4.learn_with_paraphrases(cue_list, target_embs[i], memory_id=i)
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for i in range(2000):
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mem4.cue_embs.append(bg_cue_embs[i])
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mem4.target_embs.append(bg_target_embs[i])
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mem4.memory_ids.append(100+i)
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evaluate(mem4, "Paraphrase aug (hand-crafted)")
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# Method 5: Noise + Paraphrase combined
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mem5 = AugmentedHopfield(n_augments=5, noise_std=0.15)
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for i in range(len(pairs)):
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cue_list = [cue_embs[i]]
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if i in extra_embs:
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cue_list.extend([e for e in extra_embs[i]])
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mem5.learn_with_paraphrases(cue_list, target_embs[i], memory_id=i)
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for i in range(2000):
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mem5.cue_embs.append(bg_cue_embs[i])
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mem5.target_embs.append(bg_target_embs[i])
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mem5.memory_ids.append(100+i)
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evaluate(mem5, "Noise + Paraphrase combined")
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def main():
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print("=" * 60)
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print("Experiment 7e: Cue Augmentation")
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print("=" * 60)
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model = load_model()
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test_augmentation(model)
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if __name__ == "__main__":
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main()
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