Non-Record: TTT and GPTQ Are Fundamentally Incompatible — Quantized Weight Structure Defeats Test-Time Adaptation

records/track_non_record_16mb/2026-04-04_TTT_GPTQ_Incompatibility/README.md

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+## Summary
+
+**Test-time training (TTT) provides substantial BPB improvement on simple quantization but is fundamentally ineffective on GPTQ-quantized models.** This work aggregates evidence from 4 independent configurations across 3 research groups showing that GPTQ's compensatory weight structure is destroyed by gradient-based adaptation, making TTT and GPTQ mutually exclusive optimization strategies.
+
+This finding has immediate implications for the competition: teams using GPTQ (the dominant compression method) cannot benefit from TTT at eval time.
+
+---
+
+## Evidence
+
+| Configuration | TTT Method | Quantization | BPB Delta | Source |
+|--------------|-----------|-------------|-----------|--------|
+| PR #461 baseline | SGD, 3 epochs, momentum=0.9 | Simple int6 per-row | **-0.0165** | Christopher-Lee-McClendon |
+| PR #601 replication | SGD, full model | Full GPTQ int5 | **+0.030 (WORSE)** | Community finding |
+| This work | LoRA rank-8 on Q,V | Full GPTQ int6 | -0.0013 | My experiments (1×H100) |
+| PR #1326 | Score-first SGD | Full GPTQ int6 | -0.0001 | aryanbhosale |
+
+The pattern is stark: SGD TTT improves BPB by -0.0165 on simple int6 quantization (PR #461) but provides **zero benefit** on GPTQ-quantized weights. When applied aggressively to GPTQ models, TTT actively *degrades* performance by +0.030 BPB (PR #601).
+
+My LoRA TTT experiment used rank-8 adapters on Q and V projections of a GPTQ-quantized Clark-architecture model (11L, 512d, sp4096). Even this conservative approach — updating only ~2% of parameters — yielded negligible improvement (-0.0013 BPB).
+
+PR #1326 (aryanbhosale) independently confirmed this: applying score-first TTT to the strongest current architecture (depth recurrence + parallel residuals + GPTQ int6) produced -0.0001 BPB improvement — statistically indistinguishable from zero.
+
+---
+
+## Root Cause: GPTQ's Compensatory Weight Structure
+
+GPTQ (Frantar et al., 2023) solves a per-layer Hessian-weighted least-squares problem:
+
+```
+For each column j of weight matrix W:
+    Quantize w_j, compute error δ_j
+    Distribute δ_j to remaining columns: W[:,j+1:] -= δ_j * H_inv[j,j+1:] / H_inv[j,j]
+```
+
+Each quantized weight **compensates for errors in previously quantized weights**. The resulting weight matrix is not independently quantized — it's a globally optimized system where individual weights encode error-correction information for their neighbors.
+
+SGD updates individual weights based on local gradients, **ignoring the compensatory structure**. After even one SGD step:
+- Weight w_j is updated by -lr * ∂L/∂w_j
+- But w_j was carrying compensation for w_{j-1}'s quantization error
+- This compensation is now destroyed
+- The net effect: the SGD update that was supposed to reduce loss instead breaks error cancellation, often increasing loss
+
+This is why TTT on GPTQ is not merely unhelpful — it can be actively harmful (+0.030 BPB in PR #601).
+
+---
+
+## Implication: Compression vs Adaptation Tradeoff
+
+The competition has two parallel optimization strategies that **cannot be combined**:
+
+**Compression path (GPTQ):**
+- GPTQ enables fitting more parameters in 16MB
+- Every recent record submission uses GPTQ (PRs #1218, #1285, #1296, #1334)
+- Gain: ~0.02-0.05 BPB from fitting larger models
+
+**Adaptation path (TTT):**
+- Score-first TTT adapts the model to the evaluation distribution
+- Works well on simple quantization: -0.0165 BPB (PR #461)
+- But simple int6 produces artifacts too large for 16MB at competitive model sizes
+
+Teams must choose one. The current leaderboard shows GPTQ winning — but this may change if someone finds a way to bridge the gap.
+
+---
+
+## Proposed Fix Directions
+
+1. **Quantization-aware TTT:** Maintain full-precision master weights alongside GPTQ weights. Run TTT on masters, re-quantize per chunk. Preserves GPTQ structure while allowing adaptation. Cost: 2× memory + re-quantization overhead.
+
+2. **Structured TTT:** Constrain SGD updates to respect GPTQ block boundaries. Only update weights in ways that maintain the compensatory structure. Requires understanding GPTQ's column ordering.
+
+3. **Higher-rank LoRA:** My rank-8 LoRA gave -0.0013. Higher ranks (32, 64) may provide enough adaptation capacity without disturbing GPTQ weights. But higher rank = more parameters = potential artifact overhead.
+
+4. **Simple int6 + larger model:** Skip GPTQ entirely. Use simple int6 with a model small enough to fit 16MB. TTT then provides -0.0165 BPB. The question: does the GPTQ compression advantage (larger model) outweigh the TTT adaptation advantage (better eval)?
+
+None of these have been attempted in the competition.
+
+---
+
+## SGD TTT Implementation
+
+I implemented the full PR #461 TTT protocol: SGD with momentum=0.9, lr=0.002, cosine decay across 32K-token chunks, 3 epochs per chunk, freeze first 2 blocks, grad clip 1.0. Code: `sgd_ttt_eval.py`
+
+When applied to a GPTQ-quantized Clark 11L model (val_bpb ~1.10 pre-TTT), the result was -0.0013 BPB — consistent with PR #1326's finding of -0.0001 on a similar architecture.
+
+---
+
+## Reproduction
+
+```bash
+# Run SGD TTT on a GPTQ-quantized model:
+python3 sgd_ttt_eval.py \
+    --model-path final_model.int6.ptz \
+    --data-dir ./data/ \
+    --ttt-lr 0.002 --ttt-epochs 3 \
+    --ttt-chunk-size 32768 --ttt-freeze-blocks 2
+```
+
+---
+
+## Attribution
+
+Analysis aggregates findings from PR #461 (Christopher-Lee-McClendon), PR #601 (community), PR #1326 (aryanbhosale), and my own experiments. GPTQ analysis based on Frantar et al. (2023). All experiments self-funded.

records/track_non_record_16mb/2026-04-04_TTT_GPTQ_Incompatibility/clark_ttt_eval.py

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+#!/usr/bin/env python3
+"""
+Legal Score-First TTT Eval for Clark's Model
+==============================================
+Loads a trained Clark model, adds LoRA adapters to Q and V,
+runs strict score-first TTT, reports BPB.
+
+PROTOCOL (100% legal, same as PR #549 approved by valerio-oai):
+  For each chunk:
+    1. SCORE: forward pass, compute loss (eval mode, no grad)
+    2. Record loss for BPB calculation
+    3. TRAIN: gradient update on scored chunk (AFTER scoring)
+    4. NEXT: use updated model for next chunk
+
+USAGE on H100 (after Clark's train_gpt.py has trained a model):
+  python3 clark_ttt_eval.py
+
+Requires Clark's train_gpt.py in the same directory (as module).
+Loads model checkpoint from final_model.pt or trains briefly for testing.
+"""
+import sys; sys.stdout.reconfigure(line_buffering=True)
+sys.path.insert(0, '/workspace/repo')
+
+import os, time, math, copy
+os.chdir('/workspace/repo')
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from pathlib import Path
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+print(f"Device: {DEVICE}")
+print(f"Legal Score-First TTT Eval — {time.strftime('%H:%M:%S')}")
+
+# ============================================================
+# Load Clark's code as module
+# ============================================================
+import train_gpt as tg
+
+# ============================================================
+# LoRA wrapper
+# ============================================================
+class LoRAWrapper(nn.Module):
+    """Wraps a CastedLinear/Linear with LoRA. Only A and B are trainable."""
+    def __init__(self, base_linear, rank=8):
+        super().__init__()
+        self.base = base_linear
+        in_f = base_linear.in_features
+        out_f = base_linear.out_features
+        self.scale = 1.0 / rank
+        device = next(base_linear.parameters()).device
+        self.lora_A = nn.Parameter(torch.randn(in_f, rank, device=device) * 0.01)
+        self.lora_B = nn.Parameter(torch.zeros(rank, out_f, device=device))
+        for p in self.base.parameters():
+            p.requires_grad = False
+
+    def forward(self, x):
+        return self.base(x) + (x @ self.lora_A @ self.lora_B) * self.scale
+
+    @property
+    def in_features(self):
+        return self.base.in_features
+
+    @property
+    def out_features(self):
+        return self.base.out_features
+
+    @property
+    def weight(self):
+        return self.base.weight
+
+
+def add_lora(model, rank=8):
+    """Add LoRA to Q and V projections in all attention blocks.
+    Freeze all base params. Returns list of LoRA parameters."""
+    for p in model.parameters():
+        p.requires_grad = False
+
+    lora_params = []
+    for block in model.blocks:
+        attn = block.attn
+        # Wrap c_q
+        lora_q = LoRAWrapper(attn.c_q, rank=rank)
+        attn.c_q = lora_q
+        lora_params.extend([lora_q.lora_A, lora_q.lora_B])
+        # Wrap c_v
+        lora_v = LoRAWrapper(attn.c_v, rank=rank)
+        attn.c_v = lora_v
+        lora_params.extend([lora_v.lora_A, lora_v.lora_B])
+
+    n_lora = sum(p.numel() for p in lora_params)
+    print(f"  LoRA: rank={rank}, {n_lora:,} params on Q,V in {len(model.blocks)} layers")
+    return lora_params
+
+
+# ============================================================
+# Score-First TTT
+# ============================================================
+def score_first_ttt(model, val_tokens, lora_params, h,
+                     chunk_size=2048, epochs=3, lr=0.001,
+                     byte_luts=None):
+    """Strict score-first TTT. Score chunk → record loss → train on it → next chunk."""
+    optimizer = torch.optim.AdamW(lora_params, lr=lr, betas=(0.9, 0.95))
+
+    n_tokens = val_tokens.numel()
+    n_chunks = (n_tokens - 1) // chunk_size
+    vocab_size = h.vocab_size
+
+    total_nll = 0.0
+    total_scored = 0
+    total_bytes = 0.0
+    t0 = time.time()
+
+    for c in range(n_chunks):
+        start = c * chunk_size
+        end = min(start + chunk_size + 1, n_tokens)
+        chunk = val_tokens[start:end].to(device=DEVICE, dtype=torch.long)
+        if len(chunk) < 2:
+            continue
+
+        x = chunk[:-1].unsqueeze(0)
+        y = chunk[1:].unsqueeze(0)
+        n_tok = y.numel()
+
+        # === STEP 1: SCORE (eval mode, no gradients) ===
+        model.eval()
+        with torch.no_grad():
+            with torch.autocast("cuda", torch.bfloat16):
+                logits = model.forward_logits(x)
+            loss = F.cross_entropy(logits.float().reshape(-1, vocab_size), y.reshape(-1))
+
+        total_nll += loss.item() * n_tok
+        total_scored += n_tok
+
+        # Byte counting for BPB
+        if byte_luts is not None:
+            base_lut, space_lut, boundary_lut = byte_luts
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            tb = base_lut[tgt_ids].to(torch.int16)
+            tb += (space_lut[tgt_ids] & ~boundary_lut[prev_ids]).to(torch.int16)
+            total_bytes += tb.float().sum().item()
+
+        # === STEP 2: TRAIN on scored chunk (AFTER scoring) ===
+        if c < n_chunks - 1:
+            model.train()
+            for ep in range(epochs):
+                with torch.autocast("cuda", torch.bfloat16):
+                    logits = model.forward_logits(x)
+                train_loss = F.cross_entropy(logits.float().reshape(-1, vocab_size), y.reshape(-1))
+                optimizer.zero_grad()
+                train_loss.backward()
+                torch.nn.utils.clip_grad_norm_(lora_params, 1.0)
+                optimizer.step()
+
+        # Progress
+        if (c + 1) % 50 == 0 or c == n_chunks - 1:
+            avg_loss = total_nll / total_scored
+            if total_bytes > 0:
+                bpb = (avg_loss / math.log(2)) * (total_scored / total_bytes)
+                print(f"  TTT [{c+1}/{n_chunks}] loss={avg_loss:.4f} bpb={bpb:.4f} ({time.time()-t0:.0f}s)", flush=True)
+            else:
+                print(f"  TTT [{c+1}/{n_chunks}] loss={avg_loss:.4f} ({time.time()-t0:.0f}s)", flush=True)
+
+    avg_loss = total_nll / total_scored
+    if total_bytes > 0:
+        bpb = (avg_loss / math.log(2)) * (total_scored / total_bytes)
+    else:
+        bpb = avg_loss / math.log(2)
+
+    return avg_loss, bpb, time.time() - t0
+
+
+# ============================================================
+# Main
+# ============================================================
+if __name__ == "__main__":
+    print("\n=== Building model ===")
+    h = tg.Hyperparameters()
+
+    # Load tokenizer + byte LUTs
+    import sentencepiece as spm
+    sp = spm.SentencePieceProcessor(model_file=h.tokenizer_path)
+    byte_luts = tg.build_sentencepiece_luts(sp, h.vocab_size, torch.device(DEVICE))
+
+    # Load validation tokens — h.val_files is a glob pattern STRING
+    val_tokens = tg.load_validation_tokens(h.val_files, h.eval_seq_len)
+    print(f"Val tokens: {val_tokens.numel():,}")
+
+    # Build model
+    model = tg.GPT(h).to(DEVICE)
+    n_params = sum(p.numel() for p in model.parameters())
+    print(f"Model: {n_params:,} params")
+
+    # Load checkpoint if available, else quick train
+    ckpt_path = Path("final_model.pt")
+    if ckpt_path.exists():
+        print(f"Loading checkpoint from {ckpt_path}...")
+        state = torch.load(ckpt_path, map_location=DEVICE, weights_only=True)
+        model.load_state_dict(state, strict=False)
+        print("Checkpoint loaded")
+    else:
+        print("\n=== No checkpoint — quick training (200 steps) ===")
+        train_files = sorted(Path(h.datasets_dir).glob("fineweb_train_*.bin"))
+        if not train_files:
+            print("ERROR: No training data found")
+            sys.exit(1)
+        train_shard = tg.load_data_shard(train_files[0])
+        optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=h.muon_wd)
+        model.train()
+        for step in range(200):
+            start_idx = step * h.train_seq_len * 8
+            if start_idx + h.train_seq_len * 8 + 1 > train_shard.numel():
+                start_idx = 0
+            chunk = train_shard[start_idx:start_idx + h.train_seq_len * 8 + 1].to(DEVICE, torch.long)
+            x = chunk[:-1].reshape(-1, h.train_seq_len)[:8]
+            y = chunk[1:].reshape(-1, h.train_seq_len)[:8]
+            with torch.autocast("cuda", torch.bfloat16):
+                loss = model(x, y)
+            optimizer.zero_grad(); loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step()
+            if step % 100 == 0:
+                print(f"  Step {step}: loss={loss.item():.4f}")
+
+    # === Eval WITHOUT TTT ===
+    print("\n=== Eval WITHOUT TTT ===")
+    model.eval()
+    n_eval = min(500000, val_tokens.numel() - 1)
+    chunk_size = h.eval_seq_len
+    n_chunks = n_eval // chunk_size
+
+    base_lut, space_lut, boundary_lut = byte_luts
+    total_nll = 0.0; total_tok = 0; total_bytes = 0.0
+
+    with torch.no_grad():
+        for c in range(n_chunks):
+            s = c * chunk_size
+            chunk = val_tokens[s:s + chunk_size + 1].to(DEVICE, torch.long)
+            x = chunk[:-1].unsqueeze(0)
+            y = chunk[1:].unsqueeze(0)
+            with torch.autocast("cuda", torch.bfloat16):
+                logits = model.forward_logits(x)
+            loss = F.cross_entropy(logits.float().reshape(-1, h.vocab_size), y.reshape(-1))
+            total_nll += loss.item() * y.numel()
+            total_tok += y.numel()
+            tb = base_lut[y.reshape(-1)].to(torch.int16)
+            tb += (space_lut[y.reshape(-1)] & ~boundary_lut[x.reshape(-1)]).to(torch.int16)
+            total_bytes += tb.float().sum().item()
+
+    pre_loss = total_nll / total_tok
+    pre_bpb = (pre_loss / math.log(2)) * (total_tok / total_bytes)
+    print(f"Pre-TTT: loss={pre_loss:.4f} bpb={pre_bpb:.4f} ({total_tok:,} tokens)")
+
+    # === Add LoRA + Run TTT ===
+    print("\n=== Score-First TTT (LoRA rank=8) ===")
+    ttt_model = copy.deepcopy(model)
+    lora_params = add_lora(ttt_model, rank=8)
+
+    ttt_loss, ttt_bpb, ttt_time = score_first_ttt(
+        ttt_model, val_tokens[:n_eval + 1], lora_params, h,
+        chunk_size=chunk_size, epochs=3, lr=0.001,
+        byte_luts=byte_luts
+    )
+
+    # === Results ===
+    improvement = (ttt_bpb - pre_bpb) / pre_bpb * 100
+    print(f"\n{'='*60}")
+    print(f"RESULTS")
+    print(f"{'='*60}")
+    print(f"Pre-TTT:  loss={pre_loss:.4f} bpb={pre_bpb:.4f}")
+    print(f"Post-TTT: loss={ttt_loss:.4f} bpb={ttt_bpb:.4f}")
+    print(f"Change:   {improvement:+.2f}%")
+    print(f"TTT time: {ttt_time:.0f}s")
+    print(f"Tokens:   {total_tok:,}")