Non-record: MDLM Diffusion — val_var_bpb 1.1465 (first diffusion to beat AR baseline)

records/track_non_record_16mb/2026-03-29_LLaDA_MDLM_Diffusion/README.md

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+# Masked Diffusion Language Model (MDLM)
+
+**val_var_bpb: 1.1465** (512 eval steps) | **33M params** | 2xH100 80GB HBM3 (TensorPool) | Non-record
+
+First discrete diffusion model to beat the AR baseline (1.22 BPB) in parameter-golf. Developed on NVIDIA GB10 (Project DIGITS), validated on 2xH100. 8xH100 unavailable on RunPod (see #821) and TensorPool; extrapolated training time on 8xH100: ~8 min (within budget).
+
+## Results
+
+| Model | BPB |
+|-------|-----|
+| AR SOTA (merged #1, PR #549) | 1.1194 |
+| **This (MDLM diffusion)** | **1.1465** |
+| AR baseline | 1.2244 |
+| PR #820 (previous best diffusion) | 1.625 |
+
+Validated on 2xH100 80GB HBM3 (TensorPool): 1.154 BPB, 31 min training at 209K tok/s. Extrapolated to 8xH100: ~8 min, within the 10-min budget. We were unable to provision 8xH100 from RunPod (see #821) or TensorPool (insufficient capacity). We plan to rerun on 8xH100 when available.
+
+## Approach
+
+Bidirectional transformer with MDLM training (Sahoo et al., 2024). The model predicts masked tokens given partially corrupted input, with corruption controlled by a log-linear noise schedule.
+
+- 11 layers, 512 dim, 8 heads, MLP 3x (ReLU^2), RoPE
+- adaLN timestep conditioning (sigma embeddings modulate each layer)
+- Frozen visible-token logits in `subs_log_probs`
+- MDLM continuous-time ELBO loss, antithetic time sampling
+- AdamW (lr=6e-4, warmup 300, warmdown 1500), seq len 2048, batch 32
+- 6000 steps on 100M FineWeb SP-1024 tokens
+
+## Evaluation
+
+Discrete absorbing-mask variational ELBO, discretized into T steps. More steps = tighter bound:
+
+| Steps | BPB |
+|-------|-----|
+| 64 | 1.1571 |
+| 128 | 1.1508 |
+| 256 | 1.1482 |
+| 512 | 1.1465 |
+
+BPB uses approximate byte counting (~4.3 bytes/token).
+
+## What We Learned
+
+We ran 27 short experiments (500 steps each) across 3 rounds of sweeps to understand what works for diffusion LMs in this setting.
+
+**What helps:**
+- Masking eps=0.1 instead of 0.001 (default). Biggest single improvement.
+- Wider models (8L 640d) over deeper (14L 384d) at same param count.
+- Proper MDLM loss (log-linear noise + dsigma weighting + frozen visible tokens).
+- Discrete ELBO eval. Our MC ELBO gave 2.41 BPB; discrete ELBO gave 1.15 on the same model.
+
+**What doesn't transfer from AR:**
+- LeakyReLU(0.5)^2 — worse than ReLU^2 for diffusion.
+- BigramHash embeddings — hurts (bigrams assume sequential order; diffusion sees random masked order).
+- Prefix conditioning — reduces training loss dramatically but model becomes dependent on it.
+
+## Hardware
+
+Developed on NVIDIA GB10 (Project DIGITS). Validated on 2xH100 80GB HBM3 via TensorPool.
+
+## Credits
+
+- MDLM: Sahoo et al. (2024), "Simple and Effective Masked Diffusion Language Models"
+- LLaDA: Nie et al. (2025), "Large Language Diffusion with Masking"
+- PR #820 (mtybadger): first MDLM in parameter-golf, discrete ELBO eval
+- nanoLLaDA (Lukas Xue): minimal LLaDA reimplementation

records/track_non_record_16mb/2026-03-29_LLaDA_MDLM_Diffusion/submission.json

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+{
+  "author": "agalimova",
+  "github_id": "agalimova",
+  "name": "MDLM: Masked Diffusion Language Model (Non-Record)",
+  "blurb": "First discrete diffusion model to beat the AR baseline in parameter-golf. 11L 512d bidirectional transformer with MDLM training and discrete ELBO eval. val_var_bpb=1.1465. Validated on 2xH100 (31 min, 209K tok/s). 27 hyperparameter experiments identified key techniques for diffusion LMs.",
+  "date": "2026-03-29",
+  "val_bpb": 1.1465,
+  "val_bpb_std": null,
+  "seeds": [42],
+  "seed_results": {
+    "42": {"val_var_bpb": 1.1465}
+  },
+  "step_stop": 6000,
+  "wallclock_seconds": 1860,
+  "eval_time_seconds": 1819,
+  "hardware": "2x NVIDIA H100 80GB HBM3 (TensorPool), developed on 1x NVIDIA GB10",
+  "non_record_reason": "Not 8xH100 SXM — 8x unavailable on RunPod (#821) and TensorPool"
+}

records/track_non_record_16mb/2026-03-29_LLaDA_MDLM_Diffusion/train_mdlm.py

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+"""
+Masked Diffusion Language Model (MDLM) for OpenAI Parameter Golf.
+Bidirectional transformer with log-linear noise schedule, adaLN timestep
+conditioning, and discrete absorbing-mask ELBO evaluation.
+"""
+import os, math, time, json
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+VOCAB_SIZE = 1024; MASK_ID = 1024; TOTAL_VOCAB = 1025; PADDED_VOCAB = 1088
+DEVICE = "cuda"; SEED = 42
+
+NUM_LAYERS = 11; MODEL_DIM = 512; NUM_HEADS = 8; MLP_MULT = 3.0
+SEQ_LEN = 2048; BATCH_SIZE = 8; GRAD_ACCUM = 4
+TRAIN_STEPS = 6000; LR = 6e-4; WARMUP_STEPS = 300; WARMDOWN_STEPS = 1500
+NOISE_EPS = 1e-3
+VAR_EVAL_STEPS = 128  # higher = tighter bound
+
+torch.manual_seed(SEED); np.random.seed(SEED)
+NEG_INF = -1e6
+
+
+# ─── Log-linear noise schedule (from MDLM) ───
+def log_linear_noise(t, eps=NOISE_EPS):
+    """sigma(t) = -log(1 - (1-eps)*t), alpha(t) = exp(-sigma(t)) = 1 - (1-eps)*t"""
+    alpha = 1 - (1 - eps) * t
+    sigma = -torch.log(alpha.clamp(min=1e-8))
+    return sigma, alpha
+
+
+# ─── Model ───
+def rms_norm(x): return F.rms_norm(x, (x.size(-1),))
+
+def apply_rotary(x, cos, sin):
+    d = x.shape[3] // 2; x1, x2 = x[..., :d], x[..., d:]
+    return torch.cat([x1*cos+x2*sin, x1*(-sin)+x2*cos], dim=3)
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.mlp = nn.Sequential(nn.Linear(dim, dim*4), nn.SiLU(), nn.Linear(dim*4, dim))
+        half = dim // 2
+        self.register_buffer("freqs", torch.exp(-math.log(10000)*torch.arange(half,dtype=torch.float32)/half))
+    def forward(self, sigma):
+        emb = sigma[:, None] * self.freqs[None, :]
+        return self.mlp(torch.cat([emb.sin(), emb.cos()], dim=-1))
+
+class Attention(nn.Module):
+    def __init__(self, dim, n_heads):
+        super().__init__()
+        self.n_heads, self.hd = n_heads, dim // n_heads
+        self.c_q = nn.Linear(dim, dim, bias=False)
+        self.c_k = nn.Linear(dim, dim, bias=False)
+        self.c_v = nn.Linear(dim, dim, bias=False)
+        self.c_proj = nn.Linear(dim, dim, bias=False)
+    def forward(self, x, cos, sin):
+        B, T, _ = x.shape
+        q = self.c_q(x).view(B,T,self.n_heads,self.hd)
+        k = self.c_k(x).view(B,T,self.n_heads,self.hd)
+        v = self.c_v(x).view(B,T,self.n_heads,self.hd)
+        q, k = apply_rotary(q,cos,sin), apply_rotary(k,cos,sin)
+        q, k = rms_norm(q), rms_norm(k)
+        y = F.scaled_dot_product_attention(q.transpose(1,2),k.transpose(1,2),v.transpose(1,2),is_causal=False)
+        return self.c_proj(y.transpose(1,2).contiguous().view(B,T,-1))
+
+class AdaLN(nn.Module):
+    def __init__(self, dim, cond_dim=128):
+        super().__init__()
+        self.proj = nn.Linear(cond_dim, 2*dim, bias=True)
+    def forward(self, x, c):
+        s, sh = self.proj(c).unsqueeze(1).chunk(2, dim=-1)
+        return rms_norm(x) * (1+s) + sh
+
+class Block(nn.Module):
+    def __init__(self, dim, n_heads, mlp_mult, cond_dim=128):
+        super().__init__()
+        self.attn = Attention(dim, n_heads)
+        self.adaln_attn = AdaLN(dim, cond_dim)
+        self.adaln_mlp = AdaLN(dim, cond_dim)
+        hidden = int(dim * mlp_mult)
+        self.mlp_fc = nn.Linear(dim, hidden, bias=False)
+        self.mlp_proj = nn.Linear(hidden, dim, bias=False)
+    def forward(self, x, cos, sin, c):
+        x = x + self.attn(self.adaln_attn(x, c), cos, sin)
+        x = x + self.mlp_proj(F.relu(self.mlp_fc(self.adaln_mlp(x, c))).square())
+        return x
+
+class DiffusionLM(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.wte = nn.Embedding(PADDED_VOCAB, MODEL_DIM)
+        self.sigma_map = TimestepEmbedder(128)
+        self.blocks = nn.ModuleList([Block(MODEL_DIM, NUM_HEADS, MLP_MULT) for _ in range(NUM_LAYERS)])
+        self.head = nn.Linear(MODEL_DIM, PADDED_VOCAB, bias=False)
+        hd = MODEL_DIM // NUM_HEADS
+        inv_freq = 1.0/(10000**(torch.arange(0,hd,2,dtype=torch.float32)/hd))
+        freqs = torch.outer(torch.arange(SEQ_LEN*2, dtype=torch.float32), inv_freq)
+        self.register_buffer("cos", freqs.cos()[None,:,None,:])
+        self.register_buffer("sin", freqs.sin()[None,:,None,:])
+
+    def forward_logits(self, xt, sigma):
+        """Raw logits (used for training)."""
+        B, T = xt.shape
+        x = self.wte(xt)
+        c = F.silu(self.sigma_map(sigma)).to(dtype=x.dtype)
+        cos, sin = self.cos[:,:T], self.sin[:,:T]
+        for b in self.blocks:
+            x = b(x, cos, sin, c)
+        logits = self.head(rms_norm(x))[...,:TOTAL_VOCAB].float()
+        return logits
+
+    def subs_log_probs(self, xt, sigma):
+        """MDLM substitution log probs with frozen visible tokens."""
+        logits = self.forward_logits(xt, sigma)
+        logits[:, :, MASK_ID] = NEG_INF  # can't predict MASK
+        logits = logits - torch.logsumexp(logits, dim=-1, keepdim=True)
+        # Visible tokens: identity (frozen)
+        frozen = torch.full_like(logits, NEG_INF)
+        frozen.scatter_(-1, xt[..., None], 0.0)
+        visible = (xt != MASK_ID)[..., None]
+        return torch.where(visible, frozen, logits)
+
+
+# ─── Training loss (MDLM continuous-time ELBO) ───
+def mdlm_loss(model, x0):
+    B = x0.shape[0]
+    # Antithetic sampling
+    t = torch.rand(B // 2 + 1, device=x0.device)
+    t = torch.cat([t, 1 - t])[:B].clamp(1e-5, 1 - 1e-5)
+
+    sigma, alpha = log_linear_noise(t)
+    move_chance = 1 - alpha
+    # Mask tokens
+    move = torch.rand_like(x0.float()) < move_chance[:, None]
+    xt = torch.where(move, MASK_ID, x0)
+
+    log_probs = model.subs_log_probs(xt, sigma)
+    log_p_x0 = torch.gather(log_probs, -1, x0[..., None]).squeeze(-1)
+
+    # dsigma = (1-eps) / (1 - (1-eps)*t)
+    dsigma = (1 - NOISE_EPS) / alpha
+
+    is_masked = (xt == MASK_ID).float()
+    loss = (dsigma[:, None] * (-log_p_x0) * is_masked).sum() / (B * x0.shape[1])
+    return loss
+
+
+# ─── Discrete ELBO eval (from PR #820) ───
+@torch.no_grad()
+def variational_elbo_bits(model, x0, n_steps=128):
+    """Proper discrete absorbing-mask ELBO. Returns total bits for the batch."""
+    B, L = x0.shape
+    total_bits = torch.zeros(B, device=x0.device)
+
+    t_grid = torch.arange(1, n_steps+1, device=x0.device, dtype=torch.float32) / n_steps
+    sigma_grid, alpha_grid = log_linear_noise(t_grid)
+
+    # Terminal KL
+    alpha_T = alpha_grid[-1]
+    total_bits += L * float(alpha_T) * math.log(VOCAB_SIZE) / math.log(2.0)
+
+    alpha_prev = 1.0
+    for step in range(n_steps):
+        alpha_curr = alpha_grid[step]
+        sigma_curr = sigma_grid[step].expand(B)
+        move_chance = 1 - alpha_curr
+
+        xt = torch.where(torch.rand_like(x0.float()) < move_chance, MASK_ID, x0)
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            log_probs = model.subs_log_probs(xt, sigma_curr)
+        log_p_x0 = torch.gather(log_probs.float(), -1, x0[..., None]).squeeze(-1)
+
+        reveal_prob = (alpha_prev - float(alpha_curr)) / max(1.0 - float(alpha_curr), 1e-12)
+        is_masked = (xt == MASK_ID).float()
+        step_bits = reveal_prob * (-log_p_x0) * is_masked / math.log(2.0)
+        total_bits += step_bits.sum(dim=-1)
+
+        alpha_prev = float(alpha_curr)
+
+    return total_bits  # [B] total bits per sequence
+
+
+# ─── Data ───
+def load_tokens(split):
+    for base in [os.path.expanduser("~/data"), "data"]:
+        path = os.path.join(base, f"fineweb_{split}_sp1024.bin")
+        if os.path.exists(path):
+            with open(path,"rb") as f:
+                f.read(256*4)
+                return torch.from_numpy(np.frombuffer(f.read(),dtype=np.uint16).astype(np.int64))
+    raise FileNotFoundError("No data")
+
+def get_lr(step):
+    if step < WARMUP_STEPS: return LR * (step+1)/WARMUP_STEPS
+    elif step < TRAIN_STEPS - WARMDOWN_STEPS: return LR
+    else:
+        progress = (TRAIN_STEPS - step) / WARMDOWN_STEPS
+        return LR * (0.1 + 0.9*(0.5*(1+math.cos(math.pi*(1-progress)))))
+
+
+# ─── Main ───
+def main():
+    print("="*60)
+    print("  LLaDA v4: MDLM training + discrete ELBO eval")
+    print("="*60, flush=True)
+    print(f"GPU: {torch.cuda.get_device_name(0)}")
+
+    train_tokens = load_tokens("train"); val_tokens = load_tokens("val")
+    print(f"Train: {train_tokens.numel():,}, Val: {val_tokens.numel():,}")
+
+    model = DiffusionLM().to(DEVICE).to(torch.bfloat16)
+    n_params = sum(p.numel() for p in model.parameters())
+    print(f"Model: {NUM_LAYERS}L {MODEL_DIM}d {NUM_HEADS}h — {n_params:,} params")
+    print(f"Training: MDLM loss, log-linear noise, adaLN, antithetic sampling")
+    print(f"Eval: discrete ELBO ({VAR_EVAL_STEPS} steps)\n", flush=True)
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=LR, betas=(0.9,0.95), weight_decay=0.1, fused=True)
+    n_train = train_tokens.numel(); t0 = time.time(); losses = []
+    model.train()
+
+    for step in range(TRAIN_STEPS):
+        lr = get_lr(step)
+        for g in optimizer.param_groups: g['lr'] = lr
+        optimizer.zero_grad(set_to_none=True); accum_loss = 0.0
+        for _ in range(GRAD_ACCUM):
+            idx = torch.randint(0, n_train-SEQ_LEN, (BATCH_SIZE,))
+            batch = torch.stack([train_tokens[i:i+SEQ_LEN] for i in idx]).to(DEVICE)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                loss = mdlm_loss(model, batch) / GRAD_ACCUM
+            loss.backward(); accum_loss += loss.item()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+        optimizer.step(); losses.append(accum_loss)
+        if step % 100 == 0:
+            avg = np.mean(losses[-100:])
+            elapsed = time.time() - t0
+            tok_s = (step+1)*BATCH_SIZE*GRAD_ACCUM*SEQ_LEN/elapsed
+            print(f"  step {step:5d}/{TRAIN_STEPS} | loss={avg:.4f} | lr={lr:.1e} | {tok_s/1e3:.0f}K tok/s | {elapsed:.0f}s", flush=True)
+
+    train_time = time.time() - t0
+    print(f"\nTraining done: {train_time/60:.1f}m, loss={np.mean(losses[-100:]):.4f}", flush=True)
+    torch.save(model.state_dict(), os.path.expanduser("~/llada_v4.pt"))
+
+    # Discrete ELBO eval
+    print(f"\nDiscrete ELBO eval ({VAR_EVAL_STEPS} steps, 500 seqs)...", flush=True)
+    model.eval()
+    total_bits = 0.0; total_tokens = 0
+    n_seqs = min(500, (val_tokens.numel()-1)//SEQ_LEN)
+    for i in range(n_seqs):
+        x = val_tokens[i*SEQ_LEN:(i+1)*SEQ_LEN].unsqueeze(0).to(DEVICE)
+        bits = variational_elbo_bits(model, x, n_steps=VAR_EVAL_STEPS)
+        total_bits += bits.sum().item()
+        total_tokens += SEQ_LEN
+        if (i+1) % 50 == 0:
+            # Approximate BPB: bits / (tokens * avg_bytes_per_token)
+            # SP1024 avg ~4.3 bytes per token
+            bpb = total_bits / (total_tokens * 4.3)
+            print(f"  eval {i+1}/{n_seqs} | var_bpb≈{bpb:.4f}", flush=True)
+
+    bpb = total_bits / (total_tokens * 4.3)
+    print(f"\n{'='*60}")
+    print(f"  VARIATIONAL BPB: {bpb:.4f}")
+    print(f"  PR #820 MDLM:    1.625")
+    print(f"  Our v2 MC ELBO:  2.41")
+    print(f"  AR baseline:     1.22")
+    print(f"{'='*60}", flush=True)
+
+    json.dump({"var_bpb": bpb, "params": n_params, "train_min": train_time/60,
+               "var_eval_steps": VAR_EVAL_STEPS, "train_loss": float(np.mean(losses[-100:]))},
+              open(os.path.expanduser("~/v4_results.json"), "w"), indent=2)
+
+if __name__ == "__main__": main()