like directionally something akin to stratified memory; treating time as a first class citizen, and shaping the rungs behavior to some horizon via aux loss. If we don’t figure some way to pass context coherently across horizons, we will likely be stuck in scaffolding hell. Or to say genius-in-an-inference but amnesiacs playing telephone game.
If/when you can get it working perhaps a 2 or 3 rung toy ladder w GPT-2… Continuity w text models might help w ascii text games, certain hidden-retrieval of context, and perhaps coding (setting up a curriculum to get the desired rung behavior would be tricky tho probably, esp for coding tho ><’ ) . Think tasks that would help with some level of causal thread, continuity, goals, latent memory, or plans across inference(s).
Here is some example code of an loose attempt to reward Semantic Continuity and shape rung behaviors. Be careful not to only reward explicit retrieval. :x
Something like this :
gpt->mem token emitted (or hidden state context) → CLADR
Then
CLADR AE-> R1 updated each inference
CLADR AE-> R2 updated few inferences
CLADR AE-> R3 updated every several inference
CLADR AE-> R4 updated every 10s of several inferences inference
then horizon embedding sampling from that context, and use aux loss to steer behavior for use towards semantic continuity or “why” meaning rather than ‘retrieval’ context. R1 sampling every inference effectively. Rung 2 (R2 every few inferences, unless triggered by R1 via Surprise or other gating. R3 every several inferences.
Rungs Sample the mem token emitted.
However Rungs always inform each inference of their context.
Let aux loss do rest.
Each inference would be
Chat History + [R1 + R2 + R3 + R4] → GPT
##**important note do not use Word Token Embeddings in final form. You would have to emit the latent either via a learned delimiter or out-of-band. Use mem token or similar, not WTEs when out of basic diagnosis/play tests. If you use WTE or an embedding derived directly from text you will likely squash the original representation ~~~!!!~~~** ಠ益ಠ ლ（´∀`ლ）
#goal should be something like training to use the compressive bands to get the why across to solve some task. Thus do not over sample retrieval tasks during training. Point is to have rungs learn to compress relevant information
from __future__ import annotations
"""
ChronoLadder v3.1 – same ladder, fewer foot‑guns 🪜
----------------------------------------------------
Low‑hanging‑fruit upgrades over v3.0:
• per‑rung α‑scalers (learned, init 0) that gate how much each horizon
actually injects ✨NEW
• cosine‑based contrastive InfoNCE ✨NEW
• optional latent µ‑law quantisation on slow tiers ✨NEW
• curriculum unlock: rungs τ≥64 stay frozen until step N ✨NEW
• tiny fix: _mem_gap keeps gradients alive (no detach) ✨FIX
• surprise EMA gets τ‑aware decay floor ✨TWEAK
Everything else is byte‑for‑byte v3.0 to keep merge pain low.
PyTorch ≥ 2.1, Python ≥ 3.10.
"""
import math, random, string, itertools
from typing import List
import torch, torch.nn as nn, torch.nn.functional as F
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# ─────────────────────────── Config tweaks ───────────────────────────
class CLConfig:
def __init__(
self,
tag_dim: int = 32,
use_tags: bool = True,
use_contrastive: bool = True,
use_critic: bool = False,
dropout_p: float = 0.1,
bridge_type: str = "hier_ae", # "mlp" | "hier_ae" | "attention"
promote_thresh_start: float = 1.0,
promote_cooldown_frac: float = 0.5,
use_quant: bool = True, # ✨ toggles µ‑law quant on τ≥64
quant_levels: int = 128,
curriculum_steps: int = 2000, # ✨ steps before τ≥64 go live
):
assert bridge_type in {"mlp", "hier_ae", "attention"}
self.tag_dim, self.use_tags = tag_dim, use_tags
self.use_contrastive, self.use_critic = use_contrastive, use_critic
self.dropout_p, self.bridge_type = dropout_p, bridge_type
self.promote_thresh_start, self.promote_cooldown_frac = (
promote_thresh_start, promote_cooldown_frac)
self.use_quant, self.quant_levels = use_quant, quant_levels
self.curriculum_steps = curriculum_steps
# ─────────────────────────── util helpers ────────────────────────────
def cosine_contrastive(latents: List[torch.Tensor]):
"""
InfoNCE over horizons using cosine sims instead of raw dot.
Small batch ⇒ temperature 0.07 is fine.
"""
if len(latents) < 2: # single rung – nothing to contrast
return latents[0].new_zeros([])
z = F.normalize(torch.cat(latents, 0), dim=-1)
logits = z @ z.T / 0.07
labels = torch.arange(len(latents), device=z.device)
return F.cross_entropy(logits, labels)
def mu_law_quantize(x: torch.Tensor, Q: int):
"""
Simple µ‑law uniform quantiser w/ straight‑through estimator.
"""
sgn = x.sign()
x_mu = torch.log1p(x.abs() * (Q - 1)) / math.log(Q)
x_q = torch.round(x_mu * (Q - 1)) / (Q - 1)
x_hat = sgn * (torch.expm1(x_q * math.log(Q)) / (Q - 1))
return x + (x_hat - x).detach() # STE
class SlowTierCritic(nn.Module):
def __init__(self, d): super().__init__(); self.net = nn.Sequential(
nn.Linear(d,256), nn.ReLU(), nn.Linear(256,1))
def forward(self,z): return self.net(z)
class AutoEncoder(nn.Module):
def __init__(self,in_d:int,lat_d:int):
super().__init__()
self.enc = nn.Sequential(nn.Linear(in_d,lat_d*2),nn.ReLU(),
nn.Linear(lat_d*2,lat_d))
self.dec = nn.Sequential(nn.Linear(lat_d,lat_d*2),nn.ReLU(),
nn.Linear(lat_d*2,in_d))
def encode(self,x): return self.enc(x)
def decode(self,z): return self.dec(z)
# ───────────────────────────── Memory Rung ───────────────────────────
class MemoryRung(nn.Module):
def __init__(self,name:str,tau:int,in_d:int,lat_d:int,hid:int,cfg:CLConfig,*,slots:int=1):
super().__init__()
self.name,self.tau,self.cfg = name,tau,cfg
self.ae = AutoEncoder(in_d,lat_d); self.slots = slots
tag = F.one_hot(torch.tensor(hid),cfg.tag_dim).float() if cfg.use_tags else torch.zeros(cfg.tag_dim)
self.register_buffer("tag",tag,persistent=False)
self.register_buffer("latents",torch.zeros(slots,lat_d))
self.gate = nn.Sequential(nn.Linear(in_d+lat_d+cfg.tag_dim,64),nn.GELU(),nn.Linear(64,1))
bridge_in = in_d+cfg.tag_dim+lat_d*slots
if cfg.bridge_type=="mlp":
self.bridge = nn.Sequential(nn.Linear(bridge_in,bridge_in),nn.GELU(),nn.Linear(bridge_in,in_d))
elif cfg.bridge_type=="hier_ae":
self.bridge = AutoEncoder(bridge_in,bridge_in//2)
else:
self.q_proj = nn.Linear(in_d,lat_d,bias=False)
self.k_proj = nn.Linear(bridge_in,lat_d,bias=False)
self.promote_thresh=cfg.promote_thresh_start; self.cooldown=0
self._promote=False; self.step=0; self._last_gate=torch.tensor(0.5)
def _bridge_process(self,x,lower):
if self.cfg.bridge_type=="attention" and lower:
K = F.normalize(self.k_proj(torch.stack(lower)),dim=-1)
q = F.normalize(self.q_proj(x),dim=-1)
ctx=(K@q).softmax(0).unsqueeze(-1)*K
return torch.cat([x,ctx.sum(0),self.tag],-1)
comb = torch.cat([x,*lower,self.tag],-1) if lower else torch.cat([x,self.tag],-1)
return self.bridge.encode(comb) if self.cfg.bridge_type=="hier_ae" else self.bridge(comb)
def forward(self,x,lower,*,promote_lower=False):
self.step+=1
write_now = (promote_lower and self.cooldown==0) or (self.step%self.tau==0)
if write_now:
enriched = self._bridge_process(x,lower)
prev = self.latents[0]; enc = self.ae.encode(enriched)
p = torch.sigmoid(self.gate(torch.cat([enriched,prev],-1)))
new_lat = p*enc + (1-p)*prev
if self.training and torch.rand(())<self.cfg.dropout_p: new_lat.zero_()
self.latents[0]=new_lat.detach(); self._last_gate=p.mean().detach()
# ✨ latency‑aware surprise w/ τ‑scaled EMA decay floor
surprise = F.mse_loss(enriched.detach(),self.ae.decode(enc).detach()).item()
self._promote = surprise>self.promote_thresh and self._last_gate>0.5 and self.cooldown==0
decay = 0.99 if self.tau>=64 else 0.95
self.promote_thresh = decay*self.promote_thresh + (1-decay)*surprise
if self._promote: self.cooldown=max(1,int(self.tau*self.cfg.promote_cooldown_frac))
else: self._promote=False
if self.cooldown: self.cooldown-=1
# ✨ µ‑law quantise on slow tiers to stabilise & compress
lat_out = self.latents.view(-1)
if self.cfg.use_quant and self.tau>=64:
lat_out = mu_law_quantize(lat_out, self.cfg.quant_levels)
return torch.cat([self.tag,lat_out],-1)
# ────────────────────────────── ChronoLadder LM ─────────────────────────────
class ChronoLadderLM(nn.Module):
def __init__(self,cfg:CLConfig|None=None,backbone="gpt2-medium"):
super().__init__(); self.cfg=cfg or CLConfig()
self.backbone = GPT2LMHeadModel.from_pretrained(backbone)
h=self.backbone.config.n_embd
self.rungs = nn.ModuleList([
MemoryRung("AE1",1,h,256,0,self.cfg),
MemoryRung("AE4",4,h,512,1,self.cfg),
MemoryRung("AE16",16,h,768,2,self.cfg,slots=2),
MemoryRung("AE64",64,h,1024,3,self.cfg,slots=2),
MemoryRung("AE256",256,h,2048,4,self.cfg),
])
# ✨ per‑horizon learned α‑scalers (init 0 → gradual opt‑in)
self.alphas = nn.Parameter(torch.zeros(len(self.rungs)))
fused=sum(r.latents.numel()+self.cfg.tag_dim for r in self.rungs)
self.mem_proj = nn.Sequential(nn.Linear(fused,h),nn.LayerNorm(h))
self.critics = nn.ModuleDict({r.name:SlowTierCritic(r.latents.size(-1))
for r in self.rungs if r.tau>=64}) if self.cfg.use_critic else nn.ModuleDict()
def collect_gate_entropy(self):
return sum(-(p:=r._last_gate.clamp(1e-5,1-1e-5))*p.log()-(1-p)*(1-p).log()
for r in self.rungs)/len(self.rungs)
def zero_all_latents(self):
for r in self.rungs: r.latents.zero_()
def forward(self,ids,hidden,*,step:int=0):
lower=[]; all_lat=[]; promote=False
for idx,r in enumerate(self.rungs):
# ✨ curriculum: skip slow tiers until global step unlock
if step < self.cfg.curriculum_steps and r.tau>=64:
lat = torch.cat([r.tag,
torch.zeros_like(r.latents.view(-1))], -1)
lower.append(lat); all_lat.append(lat); continue
lat=r(hidden.detach(),lower,promote_lower=promote)
promote=r._promote; lower.append(lat); all_lat.append(lat)
# apply learned α gates
gated = [torch.tanh(a)*l for a,l in zip(self.alphas,all_lat)]
mem=self.mem_proj(torch.cat(gated,-1))
out=self.backbone(inputs_embeds=hidden+mem,labels=ids)
return out.loss,all_lat
# ─────────────────────────────── Trainer ────────────────────────────
class Trainer:
def __init__(self,model:ChronoLadderLM,tok:GPT2Tokenizer,*,device=None,
λ_mem=0.1,λ_ent=0.02,λ_prom=0.01,λ_orth=0.01):
self.m=model.to(device or ('cuda' if torch.cuda.is_available() else 'cpu'))
self.tok=tok; self.opt=torch.optim.AdamW(self.m.parameters(),lr=3e-5)
self.device=next(self.m.parameters()).device
self.λ_mem, self.λ_ent, self.λ_prom, self.λ_orth = λ_mem,λ_ent,λ_prom,λ_orth
self.step_n = 0
def _mem_gap(self,ids,h,lm_with):
saved=[r.latents.clone() for r in self.m.rungs]; self.m.zero_all_latents()
lm_no,_=self.m(ids,h,step=self.step_n) # ✨ keep grads alive
for r,b in zip(self.m.rungs,saved): r.latents.copy_(b)
return (lm_no-lm_with).clamp(min=0)
def _orth_loss(self):
loss=0.
for r in self.m.rungs:
if r.latents.size(0)<2: continue
z=F.normalize(r.latents,dim=-1); gram=z@z.T
loss += (gram - torch.eye(z.size(0),device=z.device)).pow(2).mean()
return loss
def step(self,prompts:List[str]):
self.step_n += 1
ids=self.tok(prompts,return_tensors='pt',padding=True).input_ids.to(self.device)
with torch.no_grad(): h=self.m.backbone.transformer.wte(ids)
lm_loss,lat=self.m(ids,h,step=self.step_n)
# simple recon anchor
recon=sum(F.mse_loss(r.ae.decode(r.latents),h.mean(1).expand_as(r.latents))
for r in self.m.rungs)*0.1
contr=cosine_contrastive([l.view(1,-1) for l in lat])*0.05 \
if self.m.cfg.use_contrastive else torch.tensor(0.,device=self.device)
ent=self.m.collect_gate_entropy()*self.λ_ent
prom_tax=sum(int(r._promote) for r in self.m.rungs)*self.λ_prom
mem_gap=self._mem_gap(ids,h,lm_loss)*self.λ_mem; mem_loss=-mem_gap
orth=self._orth_loss()*self.λ_orth
critic=torch.tensor(0.,device=self.device)
if self.m.cfg.use_critic:
for r,l in zip(self.m.rungs,lat):
if r.name in self.m.critics:
critic += F.mse_loss(self.m.critics[r.name](l.detach()),
torch.zeros_like(self.m.critics[r.name](l)))*0.02
total=lm_loss+recon+contr+ent+prom_tax+mem_loss+orth+critic
total.backward(); nn.utils.clip_grad_norm_(self.m.parameters(),1.0)
self.opt.step(); self.opt.zero_grad()
return dict(step=self.step_n,total=total.item(),task=lm_loss.item(),
recon=recon.item(),nce=contr.item(),entropy=ent.item(),
mem_bonus=mem_gap.item(),prom_tax=prom_tax,orth=orth.item())
# ───────────────────────────── Toy demo ─────────────────────────────
##**important note do not use Word Token Embeddings in final form. You would have to emit the latent either via a learned delimiter or out-of-band. ~~~!!!~~~**
#goal should be something like training to use the compressive bands to get the why across to solve some task. Thus do not over sample retrieval tasks during training. Point is to have rungs learn to compress relevant information
def make_copy_dataset(delay=64,size=4000):
abc=list(string.ascii_lowercase)
return [f"remember {random.choice(abc)} then wait {delay} steps "+"x "*delay+"now what?"
for _ in range(size)]
if __name__=="__main__":
tok=GPT2Tokenizer.from_pretrained("gpt2-medium")
model=ChronoLadderLM(CLConfig())
trainer=Trainer(model,tok)
data=make_copy_dataset()
for _ in range(300):
stats=trainer.step(random.sample(data,4))
if stats['step']%25==0:
print(f"{stats['step']:03d} | tot {stats['total']:.3f} "
f"task {stats['task']:.3f} gap {stats['mem_bonus']:.3f} "
f"orth {stats['orth']:.3f} prom {stats['prom_tax']}")
```##**important note do not use Word Token Embeddings in final form. You would have to emit the latent either via a learned delimiter or out-of-band to then be used by your stratified memory system. When doing actual run use a mem token to write out a hidden state, do not use a squashed rep otherwise you'll inject a stateless break. ~~~!!!~~~**
#goal should be something like training to use the compressive bands to get the why across to solve some task. Thus do not over sample retrieval tasks during training. Point is to have rungs learn to compress relevant information
lol
Cadence-Ladder AEs + Temporal Signature Hybrid Memory –
Purpose
Core Ideas
Cadence Ladder Breakdown
Temporal Design Features
Training & Loss Strategy
Known Pitfalls + Solutions
Design Knobs for Exploration
Model Recommendation Is the Sweet Spot (Model Recommendation)
Overview
Example Cadence Stack
How It Works
Use-Cases
Suggested Training Curriculum
TL;DR
1. Scale-Up: Higher-Dimensional Slots for Perceptual Memory
3. Memory Fusion: Cross-Modality Latent Binding
4. Expand Temporal Reach — Add AE@1024 or AE@4096