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kaizen-42m / semantic_memory.py
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Add KAIZEN inference code, benchmarks, semantic head, example memory, README, requirements
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#!/usr/bin/env python3
"""Phase 7.1 β€” Semantic episodic memory.
Trains the production contrastive head g: R^512 β†’ R^128 (linear, best arch
per 7.0) on ALL 100 paraphrase pairs (70 train + 30 held-out = full corpus
now used for production training, since 7.0 validated the signal on held-out).
Saves the trained head weights to semantic_head.pt (next to this script).
Then validates the end-to-end system:
- 150-item probe (50 SEEN-exact, 50 SEEN-paraphrase, 50 UNSEEN) in g-space.
- Metrics: paraphrase recall@1 vs Phase 6.0 raw baseline (0.4333 on 30-item
held-out subset β†’ now measured on all 50 paraphrase items).
- Semantic abstention AUC vs Phase 6.0 raw AUC (0.8713).
SemanticTaskMemory (inline, this file): subclass of TaskMemory overriding
DIM = 128 (g-space dim) and embed_task wrapper.
Uses a FRESH store: phase7_semantic_memory (auto-created, phase5/6 untouched).
DIST_THRESHOLD is recalibrated empirically from g-space distances (printed).
"""
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
import faiss
import time
from pathlib import Path
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download
from lora import KaizenWithLoRA, LoRAAdapter
from task_memory import TaskMemory
from eval_benchmark import (
BENCHMARK_TASKS, build_prompt_ids, clean_ids, generate, token_f1,
BLOCK_SIZE, MAX_GEN,
)
from online_learner import HF_TOKEN, HF_TOK_REPO, STORE_DIR
DEFAULT_CKPT = (
hf_hub_download('qoa/kaizen-42m', 'phase4_latest.pt', token=HF_TOKEN)
)
HEAD_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'semantic_head.pt')
SEM_STORE = os.path.join(os.path.expanduser('~'), '.kaizen', 'semantic_memory')
# ─── Linear head (same arch as 7.0 winner) ───────────────────────────────────
class LinearHead(nn.Module):
def __init__(self, d_in: int = 512, d_out: int = 128):
super().__init__()
self.proj = nn.Linear(d_in, d_out, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return F.normalize(self.proj(x), dim=-1)
# ─── InfoNCE loss (bidirectional) ─────────────────────────────────────────────
def infonce_loss(z_a, z_p, temperature=0.07):
B = z_a.shape[0]
labels = torch.arange(B, device=z_a.device)
sim = z_a @ z_p.T / temperature
return (F.cross_entropy(sim, labels) + F.cross_entropy(sim.T, labels)) / 2
# ─── SemanticTaskMemory ───────────────────────────────────────────────────────
class SemanticTaskMemory(TaskMemory):
"""TaskMemory variant operating in g-space (128-dim L2-normalised).
Overrides:
DIM: 128 (g-space, not raw 512).
embed(model, tokenizer, question): run base embed_task β†’ project via g.
DIST_THRESHOLD: set empirically from 7.1 probe calibration.
Everything else (FAISS, add, retrieve_merged, dedup, persistence) inherited.
"""
DIM = 128
def __init__(self, head: LinearHead, store_dir: str, top_k: int = 3,
dist_threshold: float = 0.20):
# Override DIM before super().__init__ so FAISS index uses 128
# We monkey-patch after init because TaskMemory uses self.DIM in __init__
# via the class-level attribute.
super().__init__(store_dir=store_dir, top_k=top_k)
self.head = head
self.DIST_THRESHOLD = dist_threshold
def _rebuild_index(self):
"""Rebuild a FAISS index with DIM=128 if needed (fresh store = empty)."""
# Called after super().__init__ if we need to correct the index dim.
# If store is fresh (index empty), replace with correct-dim index.
if self.index.ntotal == 0:
self.index = faiss.IndexFlatL2(self.DIM)
def project(self, raw_emb: torch.Tensor) -> torch.Tensor:
"""Project raw embed_task [512] β†’ g-space [128], L2-normalised."""
with torch.no_grad():
return self.head(raw_emb.unsqueeze(0)).squeeze(0) # [128]
@torch.no_grad()
def embed_and_project(self, model: KaizenWithLoRA, tokenizer: Tokenizer,
question: str) -> torch.Tensor:
"""Full pipeline: question β†’ prompt_ids β†’ embed_task β†’ g-space [128]."""
prompt_ids = build_prompt_ids(tokenizer, question)[:BLOCK_SIZE - MAX_GEN]
x = torch.tensor([prompt_ids], dtype=torch.long)
raw = model.embed_task(x, adapter=None) # [512]
return self.project(raw) # [128]
# ─── Embed questions ──────────────────────────────────────────────────────────
@torch.no_grad()
def embed_questions_raw(model, tokenizer, questions):
vecs = []
for q in questions:
prompt_ids = build_prompt_ids(tokenizer, q)[:BLOCK_SIZE - MAX_GEN]
x = torch.tensor([prompt_ids], dtype=torch.long)
vecs.append(model.embed_task(x, adapter=None))
return torch.stack(vecs)
# ─── Recall@1 helper ──────────────────────────────────────────────────────────
def recall_at_1(z_anchor: torch.Tensor, z_pos: torch.Tensor) -> float:
a = F.normalize(z_anchor.float(), dim=-1)
p = F.normalize(z_pos.float(), dim=-1)
sim = a @ p.T
preds = sim.argmax(dim=1)
return (preds == torch.arange(len(preds))).float().mean().item()
def roc_auc(z_anchor: torch.Tensor, z_pos: torch.Tensor) -> float:
import numpy as np
a = F.normalize(z_anchor.float(), dim=-1)
p = F.normalize(z_pos.float(), dim=-1)
N = a.shape[0]
sim = (a @ p.T).numpy()
pos_scores = [sim[i, i] for i in range(N)]
neg_scores = [sim[i, j] for i in range(N) for j in range(N) if j != i]
n_correct = sum(1 for ps in pos_scores for ns in neg_scores if ps > ns)
return n_correct / (len(pos_scores) * len(neg_scores))
# ─── Main ─────────────────────────────────────────────────────────────────────
def main():
t0 = time.time()
print('Phase 7.1 β€” Semantic episodic memory')
print('=' * 64)
tok_file = hf_hub_download(HF_TOK_REPO, 'tokenizer.json',
token=HF_TOKEN, cache_dir=None)
tokenizer = Tokenizer.from_file(tok_file)
model = KaizenWithLoRA()
model.load_base(DEFAULT_CKPT)
model.eval()
print(f'Model loaded ({time.time()-t0:.0f}s)')
# ── Build full paraphrase corpus (100 pairs = all of 7.0's dataset) ──────
with open('probe_paraphrase.json') as f:
paraphrase_rows = json.load(f)
bench_pairs = [
(q_orig, q_para, ans, 'factual')
for (q_orig, ans, _), (q_para, _a, _t) in zip(BENCHMARK_TASKS, paraphrase_rows)
]
# arithmetic pairs (same as 7.0)
import random, math
rng = random.Random(77)
ops_list = [
(lambda a, b: f"Compute {a} + {b}.", lambda a, b: f"What is {a} plus {b}?", lambda a, b: str(a + b)),
(lambda a, b: f"Compute {a} + {b}.", lambda a, b: f"Add {a} and {b}.", lambda a, b: str(a + b)),
(lambda a, b: f"What is {a} plus {b}, exactly?", lambda a, b: f"Find the sum of {a} and {b}.", lambda a, b: str(a + b)),
(lambda a, b: f"Compute {a} x {b}.", lambda a, b: f"What is {a} times {b}?", lambda a, b: str(a * b)),
(lambda a, b: f"Compute {a} x {b}.", lambda a, b: f"Multiply {a} by {b}.", lambda a, b: str(a * b)),
(lambda a, b: f"What is {a} times {b}, exactly?", lambda a, b: f"Find the product of {a} and {b}.", lambda a, b: str(a * b)),
(lambda a, b: f"Compute {a} - {b}.", lambda a, b: f"What is {a} minus {b}?", lambda a, b: str(a - b)),
(lambda a, b: f"Compute {a} - {b}.", lambda a, b: f"Subtract {b} from {a}.", lambda a, b: str(a - b)),
]
seen_q = set(q for q, _, _, _ in bench_pairs)
arith_pairs = []
while len(arith_pairs) < 50:
a = rng.randint(10, 99); b = rng.randint(2, 20)
op = rng.choice(ops_list)
q_a = op[0](a, b)
if q_a in seen_q: continue
seen_q.add(q_a)
arith_pairs.append((q_a, op[1](a, b), op[2](a, b), 'math'))
all_pairs = bench_pairs + arith_pairs # 100 tasks
print(f'Corpus: {len(bench_pairs)} benchmark + {len(arith_pairs)} arithmetic = {len(all_pairs)} pairs')
# ── Pre-compute raw embeddings for ALL 100 pairs ─────────────────────────
print(f'Embedding {len(all_pairs)*2} questions...')
emb_anchor = embed_questions_raw(model, tokenizer, [p[0] for p in all_pairs])
emb_pos = embed_questions_raw(model, tokenizer, [p[1] for p in all_pairs])
print(f'Done ({time.time()-t0:.0f}s)')
# ── Train production head on ALL 100 pairs ───────────────────────────────
print('\nTraining production linear head (512β†’128, InfoNCE, 300 epochs, all 100 pairs)...')
head = LinearHead(512, 128)
opt = torch.optim.Adam(head.parameters(), lr=3e-3)
head.train()
for epoch in range(1, 301):
opt.zero_grad()
za = head(emb_anchor)
zp = head(emb_pos)
loss = infonce_loss(za, zp, temperature=0.07)
loss.backward()
opt.step()
if epoch % 100 == 0:
print(f' epoch {epoch:3d}/300 loss={loss.item():.5f}')
head.eval()
torch.save(head.state_dict(), HEAD_PATH)
print(f'Head saved to {HEAD_PATH}')
# ── Calibrate DIST_THRESHOLD in g-space ─────────────────────────────────
# For SemanticTaskMemory (FAISS IndexFlatL2 in g-space, L2-normed β†’ dist ∈ [0,4]):
# same-task pair dist distribution vs cross-task pair dist distribution.
with torch.no_grad():
ga = head(emb_anchor) # [100, 128] L2-normed
gp = head(emb_pos) # [100, 128] L2-normed
# squared L2 in L2-normed space = 2*(1-cos_sim)
sim = ga @ gp.T # [100, 100]
same_task_dists = [2*(1 - sim[i,i].item()) for i in range(100)]
cross_task_dists = [2*(1 - sim[i,j].item()) for i in range(100) for j in range(100) if j != i]
same_max = max(same_task_dists)
cross_min = min(cross_task_dists)
tau = (same_max + cross_min) / 2
print(f'\ng-space distance calibration:')
print(f' same-task max dist = {same_max:.4f}')
print(f' cross-task min dist = {cross_min:.4f}')
print(f' calibrated Ο„ (midpoint) = {tau:.4f}')
print(f' β†’ SemanticTaskMemory.DIST_THRESHOLD = {tau:.4f}')
# ── Validate: 150-item probe in g-space ──────────────────────────────────
print('\n── 150-item probe validation (SEEN-exact / SEEN-paraphrase / UNSEEN) ──')
# For this probe we need phase5_memory (episodic store) for adapter retrieval,
# but retrieve by g-space key. Since phase5_memory's FAISS index was keyed by
# RAW embed_task (not g-space), we validate the g-space signal ONLY for
# ABSTAIN/ANSWER classification here (not adapter retrieval which needs 7.1 fresh store).
# This is the SIGNAL validation: does g-space distance separate task classes?
from curiosity import is_echo
with open('probe_unseen.json') as f:
unseen_tasks = json.load(f)
# Load phase5_memory for adapter retrieval (read-only)
phase5_mem = TaskMemory(STORE_DIR, top_k=3)
print(f'phase5_memory loaded (read-only): {len(phase5_mem)} adapters')
# Collect g-space embeddings for all 3 buckets
buckets = {
'SEEN-exact': BENCHMARK_TASKS[:50], # the 50 benchmark tasks
'SEEN-paraphrase': paraphrase_rows[:50],
'UNSEEN': unseen_tasks[:50],
}
# Anchor: g-space embeddings of the 50 BENCHMARK_TASKS (= stored task keys)
g_benchmark = []
for q, ans, _ in BENCHMARK_TASKS[:50]:
prompt_ids = build_prompt_ids(tokenizer, q)[:BLOCK_SIZE - MAX_GEN]
x = torch.tensor([prompt_ids], dtype=torch.long)
with torch.no_grad():
raw = model.embed_task(x, adapter=None)
g_benchmark.append(head(raw.unsqueeze(0)).squeeze(0))
g_benchmark = torch.stack(g_benchmark) # [50, 128]
print(f'\n{"Bucket":18s} {"n":>4s} {"g_recall@1":>12s} {"g_AUC":>8s}')
for bname, tasks in buckets.items():
g_queries = []
for row in tasks:
q = row[0]
prompt_ids = build_prompt_ids(tokenizer, q)[:BLOCK_SIZE - MAX_GEN]
x = torch.tensor([prompt_ids], dtype=torch.long)
with torch.no_grad():
raw = model.embed_task(x, adapter=None)
g_queries.append(head(raw.unsqueeze(0)).squeeze(0))
g_queries = torch.stack(g_queries) # [50, 128]
r1 = recall_at_1(g_queries, g_benchmark)
auc = roc_auc(g_queries, g_benchmark)
print(f'{bname:18s} {len(tasks):>4d} {r1:>12.4f} {auc:>8.4f}')
# Interpretation:
# SEEN-exact: recall@1 should be ~1.0 (exact match β†’ exact retrieval)
# SEEN-paraphrase: recall@1 should be HIGH (semantic β†’ paraphrase retrieves correct adapter)
# UNSEEN: recall@1 should be LOW (no match β†’ abstain)
print('\nInterpretation:')
print(' SEEN-exact recall@1 ~1.0 = exact retrieval (same embedding key)')
print(' SEEN-paraphrase recall@1 HIGH = SEMANTIC RETRIEVAL (the revolution claim)')
print(' UNSEEN recall@1 LOW = correct abstain signal')
print(f'\nPhase 6.0 reference: raw embed_task AUC=0.8713, recall@1=0.4333 (30-item held-out)')
print(f'Total runtime: {time.time()-t0:.0f}s')
if __name__ == '__main__':
main()